The overall goal of this project is to build a word recognizer for American Sign Language video sequences, demonstrating the power of probabalistic models. In particular, this project employs hidden Markov models (HMM's) to analyze a series of measurements taken from videos of American Sign Language (ASL) collected for research (see the RWTH-BOSTON-104 Database). In this video, the right-hand x and y locations are plotted as the speaker signs the sentence.
The raw data, train, and test sets are pre-defined. You will derive a variety of feature sets (explored in Part 1), as well as implement three different model selection criterion to determine the optimal number of hidden states for each word model (explored in Part 2). Finally, in Part 3 you will implement the recognizer and compare the effects the different combinations of feature sets and model selection criteria.
At the end of each Part, complete the submission cells with implementations, answer all questions, and pass the unit tests. Then submit the completed notebook for review!
A data handler designed for this database is provided in the student codebase as the AslDb class in the asl_data module. This handler creates the initial pandas dataframe from the corpus of data included in the data directory as well as dictionaries suitable for extracting data in a format friendly to the hmmlearn library. We'll use those to create models in Part 2.
To start, let's set up the initial database and select an example set of features for the training set. At the end of Part 1, you will create additional feature sets for experimentation.
In [1]:
import numpy as np
import pandas as pd
from asl_data import AslDb
asl = AslDb() # initializes the database
asl.df.head() # displays the first five rows of the asl database, indexed by video and frame
Out[1]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
video
frame
98
0
149
181
170
175
161
62
woman-1
1
149
181
170
175
161
62
woman-1
2
149
181
170
175
161
62
woman-1
3
149
181
170
175
161
62
woman-1
4
149
181
170
175
161
62
woman-1
In [2]:
asl.df.ix[98,1] # look at the data available for an individual frame
Out[2]:
left-x 149
left-y 181
right-x 170
right-y 175
nose-x 161
nose-y 62
speaker woman-1
Name: (98, 1), dtype: object
The frame represented by video 98, frame 1 is shown here:
The objective of feature selection when training a model is to choose the most relevant variables while keeping the model as simple as possible, thus reducing training time. We can use the raw features already provided or derive our own and add columns to the pandas dataframe asl.df for selection. As an example, in the next cell a feature named 'grnd-ry' is added. This feature is the difference between the right-hand y value and the nose y value, which serves as the "ground" right y value.
In [3]:
asl.df['grnd-ry'] = asl.df['right-y'] - asl.df['nose-y']
asl.df.head() # the new feature 'grnd-ry' is now in the frames dictionary
Out[3]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
video
frame
98
0
149
181
170
175
161
62
woman-1
113
1
149
181
170
175
161
62
woman-1
113
2
149
181
170
175
161
62
woman-1
113
3
149
181
170
175
161
62
woman-1
113
4
149
181
170
175
161
62
woman-1
113
In [4]:
from asl_utils import test_features_tryit
# TODO add df columns for 'grnd-rx', 'grnd-ly', 'grnd-lx' representing differences between hand and nose locations
asl.df['grnd-rx'] = asl.df['right-x'] - asl.df['nose-x']
asl.df['grnd-ly'] = asl.df['left-y'] - asl.df['nose-y']
asl.df['grnd-lx'] = asl.df['left-x'] - asl.df['nose-x']
# test the code
test_features_tryit(asl)
asl.df sample
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
grnd-lx
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
-12
1
149
181
170
175
161
62
woman-1
113
9
119
-12
2
149
181
170
175
161
62
woman-1
113
9
119
-12
3
149
181
170
175
161
62
woman-1
113
9
119
-12
4
149
181
170
175
161
62
woman-1
113
9
119
-12
Out[4]:
Correct!
In [5]:
# collect the features into a list
features_ground = ['grnd-rx','grnd-ry','grnd-lx','grnd-ly']
#show a single set of features for a given (video, frame) tuple
[asl.df.ix[98,1][v] for v in features_ground]
Out[5]:
[9, 113, -12, 119]
Now that we have a feature list defined, we can pass that list to the build_training method to collect the features for all the words in the training set. Each word in the training set has multiple examples from various videos. Below we can see the unique words that have been loaded into the training set:
In [6]:
training = asl.build_training(features_ground)
print("Training words: {}".format(training.words))
Training words: ['JOHN', 'WRITE', 'HOMEWORK', 'IX-1P', 'SEE', 'YESTERDAY', 'IX', 'LOVE', 'MARY', 'CAN', 'GO', 'GO1', 'FUTURE', 'GO2', 'PARTY', 'FUTURE1', 'HIT', 'BLAME', 'FRED', 'FISH', 'WONT', 'EAT', 'BUT', 'CHICKEN', 'VEGETABLE', 'CHINA', 'PEOPLE', 'PREFER', 'BROCCOLI', 'LIKE', 'LEAVE', 'SAY', 'BUY', 'HOUSE', 'KNOW', 'CORN', 'CORN1', 'THINK', 'NOT', 'PAST', 'LIVE', 'CHICAGO', 'CAR', 'SHOULD', 'DECIDE', 'VISIT', 'MOVIE', 'WANT', 'SELL', 'TOMORROW', 'NEXT-WEEK', 'NEW-YORK', 'LAST-WEEK', 'WILL', 'FINISH', 'ANN', 'READ', 'BOOK', 'CHOCOLATE', 'FIND', 'SOMETHING-ONE', 'POSS', 'BROTHER', 'ARRIVE', 'HERE', 'GIVE', 'MAN', 'NEW', 'COAT', 'WOMAN', 'GIVE1', 'HAVE', 'FRANK', 'BREAK-DOWN', 'SEARCH-FOR', 'WHO', 'WHAT', 'LEG', 'FRIEND', 'CANDY', 'BLUE', 'SUE', 'BUY1', 'STOLEN', 'OLD', 'STUDENT', 'VIDEOTAPE', 'BORROW', 'MOTHER', 'POTATO', 'TELL', 'BILL', 'THROW', 'APPLE', 'NAME', 'SHOOT', 'SAY-1P', 'SELF', 'GROUP', 'JANA', 'TOY1', 'MANY', 'TOY', 'ALL', 'BOY', 'TEACHER', 'GIRL', 'BOX', 'GIVE2', 'GIVE3', 'GET', 'PUTASIDE']
The training data in training is an object of class WordsData defined in the asl_data module. in addition to the words list, data can be accessed with the get_all_sequences, get_all_Xlengths, get_word_sequences, and get_word_Xlengths methods. We need the get_word_Xlengths method to train multiple sequences with the hmmlearn library. In the following example, notice that there are two lists; the first is a concatenation of all the sequences(the X portion) and the second is a list of the sequence lengths(the Lengths portion).
In [7]:
training.get_word_Xlengths('CHOCOLATE')
Out[7]:
(array([[-11, 48, 7, 120],
[-11, 48, 8, 109],
[ -8, 49, 11, 98],
[ -7, 50, 7, 87],
[ -4, 54, 7, 77],
[ -4, 54, 6, 69],
[ -4, 54, 6, 69],
[-13, 52, 6, 69],
[-13, 52, 6, 69],
[ -8, 51, 6, 69],
[ -8, 51, 6, 69],
[ -8, 51, 6, 69],
[ -8, 51, 6, 69],
[ -8, 51, 6, 69],
[-10, 59, 7, 71],
[-15, 64, 9, 77],
[-17, 75, 13, 81],
[ -4, 48, -4, 113],
[ -2, 53, -4, 113],
[ -4, 55, 2, 98],
[ -4, 58, 2, 98],
[ -1, 59, 2, 89],
[ -1, 59, -1, 84],
[ -1, 59, -1, 84],
[ -7, 63, -1, 84],
[ -7, 63, -1, 84],
[ -7, 63, 3, 83],
[ -7, 63, 3, 83],
[ -7, 63, 3, 83],
[ -7, 63, 3, 83],
[ -7, 63, 3, 83],
[ -7, 63, 3, 83],
[ -7, 63, 3, 83],
[ -4, 70, 3, 83],
[ -4, 70, 3, 83],
[ -2, 73, 5, 90],
[ -3, 79, -4, 96],
[-15, 98, 13, 135],
[ -6, 93, 12, 128],
[ -2, 89, 14, 118],
[ 5, 90, 10, 108],
[ 4, 86, 7, 105],
[ 4, 86, 7, 105],
[ 4, 86, 13, 100],
[ -3, 82, 14, 96],
[ -3, 82, 14, 96],
[ 6, 89, 16, 100],
[ 6, 89, 16, 100],
[ 7, 85, 17, 111]]), [17, 20, 12])
So far we have a simple feature set that is enough to get started modeling. However, we might get better results if we manipulate the raw values a bit more, so we will go ahead and set up some other options now for experimentation later. For example, we could normalize each speaker's range of motion with grouped statistics using Pandas stats functions and pandas groupby. Below is an example for finding the means of all speaker subgroups.
In [8]:
df_means = asl.df.groupby('speaker').mean()
df_means
Out[8]:
left-x
left-y
right-x
right-y
nose-x
nose-y
grnd-ry
grnd-rx
grnd-ly
grnd-lx
speaker
man-1
206.248203
218.679449
155.464350
150.371031
175.031756
61.642600
88.728430
-19.567406
157.036848
31.216447
woman-1
164.661438
161.271242
151.017865
117.332462
162.655120
57.245098
60.087364
-11.637255
104.026144
2.006318
woman-2
183.214509
176.527232
156.866295
119.835714
170.318973
58.022098
61.813616
-13.452679
118.505134
12.895536
To select a mean that matches by speaker, use the pandas map method:
In [9]:
asl.df['left-x-mean'] = asl.df['speaker'].map(df_means['left-x'])
asl.df.head()
Out[9]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
grnd-lx
left-x-mean
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
-12
164.661438
1
149
181
170
175
161
62
woman-1
113
9
119
-12
164.661438
2
149
181
170
175
161
62
woman-1
113
9
119
-12
164.661438
3
149
181
170
175
161
62
woman-1
113
9
119
-12
164.661438
4
149
181
170
175
161
62
woman-1
113
9
119
-12
164.661438
In [10]:
from asl_utils import test_std_tryit
# TODO Create a dataframe named `df_std` with standard deviations grouped by speaker
df_std = asl.df.groupby('speaker').std()
# test the code
test_std_tryit(df_std)
df_std
left-x
left-y
right-x
right-y
nose-x
nose-y
grnd-ry
grnd-rx
grnd-ly
grnd-lx
left-x-mean
speaker
man-1
15.154425
36.328485
18.901917
54.902340
6.654573
5.520045
53.487999
20.269032
36.572749
15.080360
0.0
woman-1
17.573442
26.594521
16.459943
34.667787
3.549392
3.538330
33.972660
16.764706
27.117393
17.328941
0.0
woman-2
15.388711
28.825025
14.890288
39.649111
4.099760
3.416167
39.128572
16.191324
29.320655
15.050938
0.0
Out[10]:
Correct!
Implement four feature sets and answer the question that follows.
normalized Cartesian coordinates
polar coordinates
delta difference
custom features
In [11]:
# TODO add features for normalized by speaker values of left, right, x, y
# Name these 'norm-rx', 'norm-ry', 'norm-lx', and 'norm-ly'
# using Z-score scaling (X-Xmean)/Xstd
features_norm = ['norm-rx', 'norm-ry', 'norm-lx','norm-ly']
features_org = ['right-x','right-y', 'left-x', 'left-y']
# zip both lists together so we can iterate as tuples
norm_org_fields = zip(features_norm, features_org)
for norm, org in norm_org_fields:
# standard score (z score) = (X - μ) / σ
# norm = (original value - mean) / standard deviation
# calculate mean
asl.df[f'{org}-mean'] = asl.df['speaker'].map(df_means[org])
# calculate standard deviation
asl.df[f'{org}-std']= asl.df['speaker'].map(df_std[org])
# calculate standard score
asl.df[norm] = (asl.df[org] - asl.df[f'{org}-mean']) / asl.df[f'{org}-std']
asl.df.head()
Out[11]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
right-x-std
norm-rx
right-y-mean
right-y-std
norm-ry
left-x-std
norm-lx
left-y-mean
left-y-std
norm-ly
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
16.459943
1.153232
117.332462
34.667787
1.663433
17.573442
-0.891199
161.271242
26.594521
0.741835
1
149
181
170
175
161
62
woman-1
113
9
119
...
16.459943
1.153232
117.332462
34.667787
1.663433
17.573442
-0.891199
161.271242
26.594521
0.741835
2
149
181
170
175
161
62
woman-1
113
9
119
...
16.459943
1.153232
117.332462
34.667787
1.663433
17.573442
-0.891199
161.271242
26.594521
0.741835
3
149
181
170
175
161
62
woman-1
113
9
119
...
16.459943
1.153232
117.332462
34.667787
1.663433
17.573442
-0.891199
161.271242
26.594521
0.741835
4
149
181
170
175
161
62
woman-1
113
9
119
...
16.459943
1.153232
117.332462
34.667787
1.663433
17.573442
-0.891199
161.271242
26.594521
0.741835
5 rows × 23 columns
In [12]:
# TODO add features for polar coordinate values where the nose is the origin
# Name these 'polar-rr', 'polar-rtheta', 'polar-lr', and 'polar-ltheta'
# Note that 'polar-rr' and 'polar-rtheta' refer to the radius and angle
features_polar = ['polar-rr', 'polar-rtheta', 'polar-lr', 'polar-ltheta']
features_polar_group = [[features_polar[0], features_polar[1]], [features_polar[2], features_polar[3]]]
features_org = [['grnd-rx','grnd-ry'], ['grnd-lx', 'grnd-ly']]
features_polar_delta = ['polar-rr-delta', 'polar-rtheta-delta', 'polar-lr-delta', 'polar-ltheta-delta']
# zip both lists together so we can iterate as array of tuples
polar_org_fields = zip(features_polar_group, features_org)
for polar, org in polar_org_fields:
# convert cartesian coordinates x, y into polar r, ϕ
r, phi = polar
x, y = org
# calculate radius r with 🐍agoras's theorem
# r = sqrt(x^2 + y^2)
asl.df[r] = (asl.df[x] ** 2 + asl.df[y] ** 2) ** 0.5
# calculate ϕ angle arctan2
# ϕ = atan2(y, x)
# we will swap y and x to give a different quadrant
asl.df[phi] = np.arctan2(asl.df[x], asl.df[y])
# calculate delta while we are here
asl.df[f'{r}-delta'] = asl.df[r].diff(periods=1)
asl.df[f'{phi}-delta'] = asl.df[phi].diff(periods=1)
asl.df = asl.df.fillna(value=0)
asl.df.head()
Out[12]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
left-y-std
norm-ly
polar-rr
polar-rtheta
polar-rr-delta
polar-rtheta-delta
polar-lr
polar-ltheta
polar-lr-delta
polar-ltheta-delta
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
26.594521
0.741835
113.35784
0.079478
0.0
0.0
119.603512
-0.100501
0.0
0.0
1
149
181
170
175
161
62
woman-1
113
9
119
...
26.594521
0.741835
113.35784
0.079478
0.0
0.0
119.603512
-0.100501
0.0
0.0
2
149
181
170
175
161
62
woman-1
113
9
119
...
26.594521
0.741835
113.35784
0.079478
0.0
0.0
119.603512
-0.100501
0.0
0.0
3
149
181
170
175
161
62
woman-1
113
9
119
...
26.594521
0.741835
113.35784
0.079478
0.0
0.0
119.603512
-0.100501
0.0
0.0
4
149
181
170
175
161
62
woman-1
113
9
119
...
26.594521
0.741835
113.35784
0.079478
0.0
0.0
119.603512
-0.100501
0.0
0.0
5 rows × 31 columns
In [13]:
# TODO add features for left, right, x, y differences by one time step, i.e. the "delta" values discussed in the lecture
# Name these 'delta-rx', 'delta-ry', 'delta-lx', and 'delta-ly'
features_delta = ['delta-rx', 'delta-ry', 'delta-lx', 'delta-ly']
features_org = ['grnd-rx','grnd-ry', 'grnd-lx', 'grnd-ly']
# zip both lists together so we can iterate as array of tuples
delta_org_fields = zip(features_delta, features_org)
for delta, org in delta_org_fields:
# get the difference with the previous time period
asl.df[delta] = asl.df[org].diff(periods=1)
asl.df = asl.df.fillna(value=0)
asl.df.head()
Out[13]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
polar-rr-delta
polar-rtheta-delta
polar-lr
polar-ltheta
polar-lr-delta
polar-ltheta-delta
delta-rx
delta-ry
delta-lx
delta-ly
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
119.603512
-0.100501
0.0
0.0
0.0
0.0
0.0
0.0
1
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
119.603512
-0.100501
0.0
0.0
0.0
0.0
0.0
0.0
2
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
119.603512
-0.100501
0.0
0.0
0.0
0.0
0.0
0.0
3
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
119.603512
-0.100501
0.0
0.0
0.0
0.0
0.0
0.0
4
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
119.603512
-0.100501
0.0
0.0
0.0
0.0
0.0
0.0
5 rows × 35 columns
In [ ]:
In [14]:
# normalize using a feature scaling equation
features_scale = ['scale-rx', 'scale-ry', 'scale-lx','scale-ly']
features_org = ['right-x','right-y', 'left-x', 'left-y']
#features_org = ['grnd-rx','grnd-ry','grnd-lx','grnd-ly']
# zip both lists together so we can iterate as tuples
scale_org_fields = zip(features_scale, features_org)
df_max = asl.df.groupby('speaker').max()
df_min = asl.df.groupby('speaker').min()
for scale, org in scale_org_fields:
# min
asl.df[f'{org}-min'] = asl.df['speaker'].map(df_min[org])
# max
asl.df[f'{org}-max'] = asl.df['speaker'].map(df_max[org])
# rescale formula
# x' = (x - min(x)) / max(x) - min(x)
asl.df[scale] = (asl.df[org] - asl.df[f'{org}-min']) / (asl.df[f'{org}-max'] - asl.df[f'{org}-min'])
asl.df.head()
Out[14]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
scale-rx
right-y-min
right-y-max
scale-ry
left-x-min
left-x-max
scale-lx
left-y-min
left-y-max
scale-ly
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
0.66087
37
179
0.971831
139
235
0.104167
84
191
0.906542
1
149
181
170
175
161
62
woman-1
113
9
119
...
0.66087
37
179
0.971831
139
235
0.104167
84
191
0.906542
2
149
181
170
175
161
62
woman-1
113
9
119
...
0.66087
37
179
0.971831
139
235
0.104167
84
191
0.906542
3
149
181
170
175
161
62
woman-1
113
9
119
...
0.66087
37
179
0.971831
139
235
0.104167
84
191
0.906542
4
149
181
170
175
161
62
woman-1
113
9
119
...
0.66087
37
179
0.971831
139
235
0.104167
84
191
0.906542
5 rows × 47 columns
In [15]:
# normalize the polar coordinates
features_polar_norm = ['polar-rr-norm', 'polar-rtheta-norm', 'polar-lr-norm', 'polar-ltheta-norm']
features_polar = ['polar-rr', 'polar-rtheta', 'polar-lr', 'polar-ltheta']
df_polar_means = asl.df.groupby('speaker').mean()
df_polar_std = asl.df.groupby('speaker').std()
# zip both lists together so we can iterate as tuples
polar_norm_fields = zip(features_polar_norm, features_polar)
for polar_norm, polar in polar_norm_fields:
# standard score (z score) = (X - μ) / σ
# norm = (original value - mean) / standard deviation
# calculate mean
asl.df[f'{polar}-mean'] = asl.df['speaker'].map(df_polar_means[polar])
# calculate standard deviation
asl.df[f'{polar}-std']= asl.df['speaker'].map(df_polar_std[polar])
# calculate standard score
asl.df[polar_norm] = (asl.df[polar] - asl.df[f'{polar}-mean']) / asl.df[f'{polar}-std']
asl.df.head()
Out[15]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
polar-rr-norm
polar-rtheta-mean
polar-rtheta-std
polar-rtheta-norm
polar-lr-mean
polar-lr-std
polar-lr-norm
polar-ltheta-mean
polar-ltheta-std
polar-ltheta-norm
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
1.573439
-0.302214
0.397837
0.959419
105.968447
25.133829
0.542499
0.060601
0.219121
-0.735219
1
149
181
170
175
161
62
woman-1
113
9
119
...
1.573439
-0.302214
0.397837
0.959419
105.968447
25.133829
0.542499
0.060601
0.219121
-0.735219
2
149
181
170
175
161
62
woman-1
113
9
119
...
1.573439
-0.302214
0.397837
0.959419
105.968447
25.133829
0.542499
0.060601
0.219121
-0.735219
3
149
181
170
175
161
62
woman-1
113
9
119
...
1.573439
-0.302214
0.397837
0.959419
105.968447
25.133829
0.542499
0.060601
0.219121
-0.735219
4
149
181
170
175
161
62
woman-1
113
9
119
...
1.573439
-0.302214
0.397837
0.959419
105.968447
25.133829
0.542499
0.060601
0.219121
-0.735219
5 rows × 59 columns
In [16]:
# scale the polar coordinates
features_polar_scale = ['polar-rr-scale', 'polar-rtheta-scale', 'polar-lr-scale', 'polar-ltheta-scale']
features_polar = ['polar-rr', 'polar-rtheta', 'polar-lr', 'polar-ltheta']
# zip both lists together so we can iterate as tuples
polar_scale_fields = zip(features_polar_scale, features_polar)
df_max = asl.df.groupby('speaker').max()
df_min = asl.df.groupby('speaker').min()
for scale, polar in polar_scale_fields:
# min
asl.df[f'{polar}-min'] = asl.df['speaker'].map(df_min[polar])
# max
asl.df[f'{polar}-max'] = asl.df['speaker'].map(df_max[polar])
# rescale formulae
# x' = (x - min(x)) / max(x) - min(x)
asl.df[scale] = (asl.df[polar] - asl.df[f'{polar}-min']) / (asl.df[f'{polar}-max'] - asl.df[f'{polar}-min'])
asl.df.head()
Out[16]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
polar-rr-scale
polar-rtheta-min
polar-rtheta-max
polar-rtheta-scale
polar-lr-min
polar-lr-max
polar-lr-scale
polar-ltheta-min
polar-ltheta-max
polar-ltheta-scale
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
0.894177
-2.498092
1.125495
0.711331
35.777088
138.75518
0.814022
-0.198946
1.153898
0.072769
1
149
181
170
175
161
62
woman-1
113
9
119
...
0.894177
-2.498092
1.125495
0.711331
35.777088
138.75518
0.814022
-0.198946
1.153898
0.072769
2
149
181
170
175
161
62
woman-1
113
9
119
...
0.894177
-2.498092
1.125495
0.711331
35.777088
138.75518
0.814022
-0.198946
1.153898
0.072769
3
149
181
170
175
161
62
woman-1
113
9
119
...
0.894177
-2.498092
1.125495
0.711331
35.777088
138.75518
0.814022
-0.198946
1.153898
0.072769
4
149
181
170
175
161
62
woman-1
113
9
119
...
0.894177
-2.498092
1.125495
0.711331
35.777088
138.75518
0.814022
-0.198946
1.153898
0.072769
5 rows × 71 columns
In [17]:
# adding additional deltas
features_delta_scale = ['scale-rx-delta', 'scale-ry-delta', 'scale-lx-delta','scale-ly-delta']
features_scale = ['scale-rx', 'scale-ry', 'scale-lx','scale-ly']
# zip both lists together so we can iterate as array of tuples
delta_scale_fields = zip(features_delta_scale, features_scale)
for delta, scale in delta_scale_fields:
# get the difference with the previous time period
asl.df[delta] = asl.df[scale].diff(periods=1)
asl.df = asl.df.fillna(value=0)
asl.df.tail()
Out[17]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
polar-lr-min
polar-lr-max
polar-lr-scale
polar-ltheta-min
polar-ltheta-max
polar-ltheta-scale
scale-rx-delta
scale-ry-delta
scale-lx-delta
scale-ly-delta
video
frame
125
52
148
181
171
156
162
60
woman-1
96
9
121
...
35.777088
138.75518
0.835422
-0.198946
1.153898
0.061911
0.017391
0.063380
0.0
0.0
53
148
181
172
165
162
60
woman-1
105
10
121
...
35.777088
138.75518
0.835422
-0.198946
1.153898
0.061911
0.008696
0.063380
0.0
0.0
54
148
181
175
173
162
60
woman-1
113
13
121
...
35.777088
138.75518
0.835422
-0.198946
1.153898
0.061911
0.026087
0.056338
0.0
0.0
55
148
181
175
173
162
60
woman-1
113
13
121
...
35.777088
138.75518
0.835422
-0.198946
1.153898
0.061911
0.000000
0.000000
0.0
0.0
56
148
181
175
173
162
60
woman-1
113
13
121
...
35.777088
138.75518
0.835422
-0.198946
1.153898
0.061911
0.000000
0.000000
0.0
0.0
5 rows × 75 columns
In [18]:
# left and right diff on x and y polar
features_polar_scale_diff = ['polar-r-diff', 'polar-theta-diff']
features_polar_scale = [['polar-rr-scale', 'polar-lr-scale'], ['polar-rtheta-scale', 'polar-ltheta-scale']]
features_polar_scale_diff_delta = ['polar-r-diff-delta', 'polar-theta-diff-delta']
# zip both lists together so we can iterate as tuples
polar_scale_diff_fields = zip(features_polar_scale_diff, features_polar_scale)
for diff, polar in polar_scale_diff_fields:
polar_r, polar_l = polar
asl.df[diff] = np.absolute(asl.df[polar_r] - asl.df[polar_l])
asl.df[f'{diff}-delta'] = asl.df[diff].diff(periods=1)
asl.df = asl.df.fillna(value=0)
asl.df.head()
Out[18]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
polar-ltheta-max
polar-ltheta-scale
scale-rx-delta
scale-ry-delta
scale-lx-delta
scale-ly-delta
polar-r-diff
polar-r-diff-delta
polar-theta-diff
polar-theta-diff-delta
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
1.153898
0.072769
0.0
0.0
0.0
0.0
0.080155
0.0
0.638562
0.0
1
149
181
170
175
161
62
woman-1
113
9
119
...
1.153898
0.072769
0.0
0.0
0.0
0.0
0.080155
0.0
0.638562
0.0
2
149
181
170
175
161
62
woman-1
113
9
119
...
1.153898
0.072769
0.0
0.0
0.0
0.0
0.080155
0.0
0.638562
0.0
3
149
181
170
175
161
62
woman-1
113
9
119
...
1.153898
0.072769
0.0
0.0
0.0
0.0
0.080155
0.0
0.638562
0.0
4
149
181
170
175
161
62
woman-1
113
9
119
...
1.153898
0.072769
0.0
0.0
0.0
0.0
0.080155
0.0
0.638562
0.0
5 rows × 79 columns
In [19]:
# left and right diff on x and y ground
features_ground_diff = ['grnd-x-diff', 'grnd-y-diff']
features_org = [['grnd-rx','grnd-lx'], ['grnd-ry', 'grnd-ly']]
features_ground_diff_delta = ['grnd-x-diff-delta', 'grnd-y-diff-delta']
# zip both lists together so we can iterate as tuples
ground_diff_fields = zip(features_ground_diff, features_org)
for diff, ground in ground_diff_fields:
ground_r, ground_l = ground
asl.df[diff] = np.absolute(asl.df[ground_r] - asl.df[ground_l])
asl.df[f'{diff}-delta'] = asl.df[diff].diff(periods=1)
asl.df = asl.df.fillna(value=0)
asl.df.head()
Out[19]:
left-x
left-y
right-x
right-y
nose-x
nose-y
speaker
grnd-ry
grnd-rx
grnd-ly
...
scale-lx-delta
scale-ly-delta
polar-r-diff
polar-r-diff-delta
polar-theta-diff
polar-theta-diff-delta
grnd-x-diff
grnd-x-diff-delta
grnd-y-diff
grnd-y-diff-delta
video
frame
98
0
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
0.080155
0.0
0.638562
0.0
21
0.0
6
0.0
1
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
0.080155
0.0
0.638562
0.0
21
0.0
6
0.0
2
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
0.080155
0.0
0.638562
0.0
21
0.0
6
0.0
3
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
0.080155
0.0
0.638562
0.0
21
0.0
6
0.0
4
149
181
170
175
161
62
woman-1
113
9
119
...
0.0
0.0
0.080155
0.0
0.638562
0.0
21
0.0
6
0.0
5 rows × 83 columns
Question 1: What custom features did you choose for the features_custom set and why?
Answer 1:
I implemented a mix of normalize with the scaling equation, delta and diffing of existing features.
The features_scale uses the scaling equation on the original right and left, x and y. The idea being that scaling reduces the difference and variation between similar patterns which hopefully would result in better recognition.
The features_delta_scale is the delta diff of the previous frame for the features_scale. The purpose of the delta is to take advantage of the time dimensional information and detect acceleration and deceleration.
The features_polar_scale is the scaling equation applied to the polar features. As above the idea being to reduce the sensitivity / variation between different data for the same words.
The features_polar_norm uses the z score to normalize the the polar features. Unlike with the scaling, we are aiming to highlight the variation in terms of the relationship between the mean and the deviation from the mean.
The features_polar_scale_diff is the diff between the left and right polar features. The idea here was to take advantage of the fact that some words use both hands and some do not, meaning that there should be unique information on how much the two hands vary from each other.
The features_polar_scale_diff_delta is the delta diff of the previous frame. As above this is the time delta one step backwards for the polar scale diff.
The features_ground_diff is the left and right diff of the ground values for x and y. As above this is a diff of the left and right hands in x and y from the ground values which are based on the diff between nose and each hand.
The features_ground_diff_delta is the previous frame delta. Additionally the time step delta for the above feature was created.
In [20]:
import unittest
# import numpy as np
class TestFeatures(unittest.TestCase):
def test_features_ground(self):
sample = (asl.df.ix[98, 1][features_ground]).tolist()
self.assertEqual(sample, [9, 113, -12, 119])
def test_features_norm(self):
sample = (asl.df.ix[98, 1][features_norm]).tolist()
np.testing.assert_almost_equal(sample, [ 1.153, 1.663, -0.891, 0.742], 3)
def test_features_polar(self):
sample = (asl.df.ix[98,1][features_polar]).tolist()
np.testing.assert_almost_equal(sample, [113.3578, 0.0794, 119.603, -0.1005], 3)
def test_features_delta(self):
sample = (asl.df.ix[98, 0][features_delta]).tolist()
self.assertEqual(sample, [0, 0, 0, 0])
sample = (asl.df.ix[98, 18][features_delta]).tolist()
self.assertTrue(sample in [[-16, -5, -2, 4], [-14, -9, 0, 0]], "Sample value found was {}".format(sample))
suite = unittest.TestLoader().loadTestsFromModule(TestFeatures())
unittest.TextTestRunner().run(suite)
....
----------------------------------------------------------------------
Ran 4 tests in 0.013s
OK
Out[20]:
<unittest.runner.TextTestResult run=4 errors=0 failures=0>
The objective of Model Selection is to tune the number of states for each word HMM prior to testing on unseen data. In this section you will explore three methods:
Now that we have built a training set with sequence data, we can "train" models for each word. As a simple starting example, we train a single word using Gaussian hidden Markov models (HMM). By using the fit method during training, the Baum-Welch Expectation-Maximization (EM) algorithm is invoked iteratively to find the best estimate for the model for the number of hidden states specified from a group of sample seequences. For this example, we assume the correct number of hidden states is 3, but that is just a guess. How do we know what the "best" number of states for training is? We will need to find some model selection technique to choose the best parameter.
In [21]:
import warnings
from hmmlearn.hmm import GaussianHMM
def train_a_word(word, num_hidden_states, features):
warnings.filterwarnings("ignore", category=DeprecationWarning)
training = asl.build_training(features)
X, lengths = training.get_word_Xlengths(word)
model = GaussianHMM(n_components=num_hidden_states, n_iter=1000).fit(X, lengths)
logL = model.score(X, lengths)
return model, logL
demoword = 'BOOK'
model, logL = train_a_word(demoword, 3, features_ground)
print("Number of states trained in model for {} is {}".format(demoword, model.n_components))
print("logL = {}".format(logL))
Number of states trained in model for BOOK is 3
logL = -2331.1138127433187
The HMM model has been trained and information can be pulled from the model, including means and variances for each feature and hidden state. The log likelihood for any individual sample or group of samples can also be calculated with the score method.
In [22]:
def show_model_stats(word, model):
print("Number of states trained in model for {} is {}".format(word, model.n_components))
variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])
for i in range(model.n_components): # for each hidden state
print("hidden state #{}".format(i))
print("mean = ", model.means_[i])
print("variance = ", variance[i])
show_model_stats(demoword, model)
Number of states trained in model for BOOK is 3
hidden state #0
mean = [ -1.12415027 69.44164191 17.02866283 77.7231196 ]
variance = [ 19.70434594 16.83041492 30.51552305 11.03678246]
hidden state #1
mean = [ -11.45300909 94.109178 19.03512475 102.2030162 ]
variance = [ 77.403668 203.35441965 26.68898447 156.12444034]
hidden state #2
mean = [ -3.46504869 50.66686933 14.02391587 52.04731066]
variance = [ 49.12346305 43.04799144 39.35109609 47.24195772]
In [23]:
my_testword = 'CHOCOLATE'
model, logL = train_a_word(my_testword, 3, features_ground) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
model, logL = train_a_word(my_testword, 5, features_ground) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
model, logL = train_a_word(my_testword, 10, features_ground) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean = [ 0.58333333 87.91666667 12.75 108.5 ]
variance = [ 39.41055556 18.74388889 9.855 144.4175 ]
hidden state #1
mean = [ -9.30211403 55.32333876 6.92259936 71.24057775]
variance = [ 16.16920957 46.50917372 3.81388185 15.79446427]
hidden state #2
mean = [ -5.40587658 60.1652424 2.32479599 91.3095432 ]
variance = [ 7.95073876 64.13103127 13.68077479 129.5912395 ]
logL = -601.3291470028636
Number of states trained in model for CHOCOLATE is 5
hidden state #0
mean = [ -9.23826304 55.30740641 6.92298855 71.30558162]
variance = [ 16.30897315 45.96991798 3.76848558 15.98402053]
hidden state #1
mean = [ 3.32089022 86.11784619 12.66977977 102.36987409]
variance = [ 12.48273231 7.43528515 12.86407411 24.73804616]
hidden state #2
mean = [ -6.37753172 51.09767101 3.64019095 104.46455217]
variance = [ 10.28279876 12.43850367 27.33782827 106.89825397]
hidden state #3
mean = [ -7.70665738 93.36393433 12.99292645 127.06351815]
variance = [ 29.53966949 13.52107768 0.66764483 48.4385572 ]
hidden state #4
mean = [ -4.93673736 64.73126904 1.62601029 84.91542811]
variance = [ 6.16147276 28.36727822 5.4706657 13.12675543]
logL = -544.2490114712311
Number of states trained in model for CHOCOLATE is 10
hidden state #0
mean = [ -8.40067795 52.59978429 6.10001541 69.20003082]
variance = [ 8.43931714 5.84159793 0.09101248 0.36104946]
hidden state #1
mean = [ 5.14285714 87.28571428 12.28571429 104.14285713]
variance = [ 1.2667347 3.34836733 15.91979596 16.40959184]
hidden state #2
mean = [ -7. 63. 2.11111116 83.22222221]
variance = [ 1.11111113e-03 1.11111113e-03 2.76654311e+00 1.73950611e-01]
hidden state #3
mean = [ -4.75090102 54.1751911 4.55673922 89.79787104]
variance = [ 1.72969128 7.97924713 6.32451768 77.55864946]
hidden state #4
mean = [ -15. 98. 13. 135.]
variance = [ 0.01 0.01 0.01 0.01]
hidden state #5
mean = [ -7.1980018 49.20640896 3.6148839 110.45570614]
variance = [ 13.28540092 3.75295248 40.02025001 55.44569677]
hidden state #6
mean = [ -2.31412779 66.86370052 1.01505022 87.17337889]
variance = [ 1.6540288 56.09246851 8.17726208 22.15604926]
hidden state #7
mean = [-15.99999999 69.49999999 11. 78.99999999]
variance = [ 1.00500004 30.25500009 4.00500001 4.00500007]
hidden state #8
mean = [ -3. 82. 14. 96.]
variance = [ 0.005 0.005 0.005 0.005]
hidden state #9
mean = [ -3.99999989 90.99999999 12.99999996 122.99999981]
variance = [ 4.00500097 4.00499996 1.0050001 25.00500253]
logL = -311.36032290597507
In [24]:
my_testword = 'CHOCOLATE'
model, logL = train_a_word(my_testword, 3, features_norm) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
model, logL = train_a_word(my_testword, 5, features_norm) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
model, logL = train_a_word(my_testword, 10, features_norm) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean = [ 1.0625066 -0.1740204 -1.31812255 -1.61791893]
variance = [ 0.05296979 0.00373254 0.03820247 0.05531266]
hidden state #1
mean = [ 0.53813142 -0.04704542 0.04545 -0.61902479]
variance = [ 0.02432711 0.04933945 0.03044391 0.13395751]
hidden state #2
mean = [ 0.38934967 -0.18383814 -0.34623283 -1.59201968]
variance = [ 0.0697732 0.02960059 0.07727065 0.06911305]
logL = 22.651977224475385
Number of states trained in model for CHOCOLATE is 5
hidden state #0
mean = [ 1.10241819 -0.18161882 -1.31638751 -1.67322035]
variance = [ 0.04076822 0.00347005 0.04198918 0.02724474]
hidden state #1
mean = [ 0.49437513 0.05588034 0.10128496 -0.81554027]
variance = [ 0.05344456 0.0278748 0.01792016 0.07558935]
hidden state #2
mean = [ 0.47584589 -0.25533689 -0.32767985 -1.72432406]
variance = [ 0.04236558 0.00442938 0.00152129 0.00670457]
hidden state #3
mean = [ 0.49157954 -0.34769502 -0.19905764 -0.3117678 ]
variance = [ 0.02972448 0.00401613 0.0093452 0.29192866]
hidden state #4
mean = [ 0.42628493 -0.05260003 -1.3028712 -0.96933571]
variance = [ 0.06204346 0.00709175 0.00609844 0.01460437]
logL = 103.40368088106393
Number of states trained in model for CHOCOLATE is 10
hidden state #0
mean = [ 0.4247135 -0.05221498 -1.3030743 -0.968205 ]
variance = [ 0.06168629 0.00708208 0.00609719 0.01429058]
hidden state #1
mean = [ 0.4241895 0.01925529 0.13939809 -0.86668331]
variance = [ 0.00111112 0.00111112 0.01006556 0.00135549]
hidden state #2
mean = [ 0.45218303 -0.25816212 -0.33235379 -1.74595539]
variance = [ 0.03905967 0.00471533 0.00138022 0.00143345]
hidden state #3
mean = [ 1.23003058 -0.17068542 -1.3455516 -1.67423174]
variance = [ 0.00279946 0.00427219 0.0567019 0.01559318]
hidden state #4
mean = [ 0.47240855 -0.3577426 -0.2248649 0.03930709]
variance = [ 0.04229115 0.00537022 0.00330059 0.06637429]
hidden state #5
mean = [ 0.62555458 -0.19222078 0.090843 -0.51651758]
variance = [ 0.01508254 0.01207985 0.00700357 0.04386182]
hidden state #6
mean = [-0.05817758 0.16808098 -0.01393997 -1.40597453]
variance = [ 0.00951053 0.02424247 0.02189111 0.00981432]
hidden state #7
mean = [ 0.71543024 -0.18773682 -0.15856366 -1.07640139]
variance = [ 0.01284624 0.01173578 0.01705401 0.07308065]
hidden state #8
mean = [ 0.80411282 -0.20698867 -1.24824245 -1.66960632]
variance = [ 0.00583755 0.00400592 0.00430488 0.05824185]
hidden state #9
mean = [ 0.65201516 0.30770731 0.11884769 -0.68703044]
variance = [ 0.00503759 0.01373268 0.04034493 0.04420975]
logL = 213.9080623031163
In [25]:
my_testword = 'CHOCOLATE'
model, logL = train_a_word(my_testword, 3, features_polar) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
model, logL = train_a_word(my_testword, 5, features_polar) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
model, logL = train_a_word(my_testword, 10, features_polar) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean = [ 56.19235087 -0.16348589 71.60640024 0.0958256 ]
variance = [ 5.16348282e+01 4.22849414e-03 1.74535632e+01 1.18341913e-03]
hidden state #1
mean = [ 8.81313612e+01 8.31771967e-03 1.09304329e+02 1.18434797e-01]
variance = [ 2.06973439e+01 5.41190376e-03 1.41647879e+02 1.92144403e-03]
hidden state #2
mean = [ 6.05010053e+01 -9.31757873e-02 9.14166393e+01 2.57297227e-02]
variance = [ 6.15523307e+01 3.56540651e-03 1.30626433e+02 1.81995354e-03]
logL = -179.2152206555334
Number of states trained in model for CHOCOLATE is 5
hidden state #0
mean = [ 6.49660812e+01 -7.66448776e-02 8.49704256e+01 1.96181830e-02]
variance = [ 2.79101318e+01 2.22783106e-03 1.33175921e+01 1.33785207e-03]
hidden state #1
mean = [ 9.39868430e+01 -8.19420621e-02 1.28036029e+02 1.01850472e-01]
variance = [ 1.69680395e+01 6.36962530e-03 4.61929559e+01 3.59020967e-03]
hidden state #2
mean = [ 8.62831931e+01 3.68064503e-02 1.03392035e+02 1.23669315e-01]
variance = [ 7.63788241e+00 2.82298201e-03 2.60749056e+01 2.37687842e-03]
hidden state #3
mean = [ 5.16058030e+01 -1.26880309e-01 1.04673109e+02 3.69179729e-02]
variance = [ 1.04668514e+01 5.88861476e-03 1.06176215e+02 3.80580239e-03]
hidden state #4
mean = [ 56.17156009 -0.16250762 71.6610455 0.0957717 ]
variance = [ 5.11178106e+01 4.27034255e-03 1.75372037e+01 1.17099160e-03]
logL = -126.87164558153644
Number of states trained in model for CHOCOLATE is 10
hidden state #0
mean = [ 6.45982654e+01 -1.00731630e-01 8.32289423e+01 2.73099173e-02]
variance = [ 6.78551493e+00 1.34868326e-03 1.36684278e-01 1.25296012e-03]
hidden state #1
mean = [ 8.76414564e+01 4.86595102e-02 1.06679064e+02 1.17764416e-01]
variance = [ 3.21979486e+00 2.11728932e-03 3.48770092e+01 2.56706853e-03]
hidden state #2
mean = [ 4.98733344e+01 -1.46538200e-01 1.10859198e+02 3.42589349e-02]
variance = [ 2.77994189e+00 7.71650533e-03 5.02489891e+01 5.55145301e-03]
hidden state #3
mean = [ 52.62385231 -0.15731319 69.26037828 0.08673834]
variance = [ 1.28499312e+00 4.36527755e-03 1.11112614e-03 1.11112614e-03]
hidden state #4
mean = [ 9.61664990e+01 -1.08144698e-01 1.32091327e+02 9.47412810e-02]
variance = [ 8.85452862e+00 6.91218433e-03 1.24964348e+01 5.00108146e-03]
hidden state #5
mean = [ 7.60414657e+01 -3.26763134e-02 9.31098502e+01 6.93141653e-03]
variance = [ 9.09694641e+00 5.02738610e-03 8.85023012e+00 7.35832807e-03]
hidden state #6
mean = [ 5.80590047e+01 -3.85459272e-02 9.06436588e+01 7.97729690e-03]
variance = [ 2.24797312e+00 2.70745809e-03 3.99264008e+01 2.27230798e-03]
hidden state #7
mean = [ 67.49270808 -0.20700505 76.96830477 0.12458847]
variance = [ 5.00635887e+01 4.10702618e-03 1.92123354e+01 3.98459792e-03]
hidden state #8
mean = [ 8.20548597e+01 -3.65690559e-02 9.70154627e+01 1.44812498e-01]
variance = [ 0.005 0.005 0.005 0.005]
hidden state #9
mean = [ 52.29769687 -0.10695158 82.4422712 0.08574809]
variance = [ 3.39103978e+00 6.04778848e-03 2.71764999e+01 5.00037262e-03]
logL = 2.9593971940567876
In [26]:
my_testword = 'CHOCOLATE'
model, logL = train_a_word(my_testword, 3, features_delta) # Experiment here with different parameters
show_model_stats(my_testword, model)
print("logL = {}".format(logL))
Number of states trained in model for CHOCOLATE is 3
hidden state #0
mean = [-0.20000837 0.63997599 0. 0. ]
variance = [ 6.16035924e+00 3.75066008e+00 4.00002314e-04 4.00002314e-04]
hidden state #1
mean = [ 1.63297181 -0.66520087 -0.05200412 -8.86200727]
variance = [ 13.36062066 6.99698076 10.13146928 13.54449171]
hidden state #2
mean = [ 0.26776402 4.82243411 0.65101707 5.29584759]
variance = [ 16.00458744 19.61210375 15.4434961 9.25789207]
logL = -250.6872100636495
We can plot the means and variances for each state and feature. Try varying the number of states trained for the HMM model and examine the variances. Are there some models that are "better" than others? How can you tell? We would like to hear what you think in the classroom online.
In [27]:
%matplotlib inline
In [28]:
import math
from matplotlib import (cm, pyplot as plt, mlab)
def visualize(word, model):
""" visualize the input model for a particular word """
variance=np.array([np.diag(model.covars_[i]) for i in range(model.n_components)])
figures = []
for parm_idx in range(len(model.means_[0])):
xmin = int(min(model.means_[:,parm_idx]) - max(variance[:,parm_idx]))
xmax = int(max(model.means_[:,parm_idx]) + max(variance[:,parm_idx]))
fig, axs = plt.subplots(model.n_components, sharex=True, sharey=False)
colours = cm.rainbow(np.linspace(0, 1, model.n_components))
for i, (ax, colour) in enumerate(zip(axs, colours)):
x = np.linspace(xmin, xmax, 100)
mu = model.means_[i,parm_idx]
sigma = math.sqrt(np.diag(model.covars_[i])[parm_idx])
ax.plot(x, mlab.normpdf(x, mu, sigma), c=colour)
ax.set_title("{} feature {} hidden state #{}".format(word, parm_idx, i))
ax.grid(True)
figures.append(plt)
for p in figures:
p.show()
visualize(my_testword, model)
Review the ModelSelector class from the codebase found in the my_model_selectors.py module. It is designed to be a strategy pattern for choosing different model selectors. For the project submission in this section, subclass SelectorModel to implement the following model selectors. In other words, you will write your own classes/functions in the my_model_selectors.py module and run them from this notebook:
SelectorCV: Log likelihood with CVSelectorBIC: BIC SelectorDIC: DICYou will train each word in the training set with a range of values for the number of hidden states, and then score these alternatives with the model selector, choosing the "best" according to each strategy. The simple case of training with a constant value for n_components can be called using the provided SelectorConstant subclass as follow:
In [29]:
from my_model_selectors import SelectorConstant
training = asl.build_training(features_ground) # Experiment here with different feature sets defined in part 1
word = 'FISH' # Experiment here with different words
model = SelectorConstant(training.get_all_sequences(), training.get_all_Xlengths(), word, n_constant=3).select()
print("Number of states trained in model for {} is {}".format(word, model.n_components))
Number of states trained in model for FISH is 3
If we simply score the model with the Log Likelihood calculated from the feature sequences it has been trained on, we should expect that more complex models will have higher likelihoods. However, that doesn't tell us which would have a better likelihood score on unseen data. The model will likely be overfit as complexity is added. To estimate which topology model is better using only the training data, we can compare scores using cross-validation. One technique for cross-validation is to break the training set into "folds" and rotate which fold is left out of training. The "left out" fold scored. This gives us a proxy method of finding the best model to use on "unseen data". In the following example, a set of word sequences is broken into three folds using the scikit-learn Kfold class object. When you implement SelectorCV, you will use this technique.
In [30]:
from sklearn.model_selection import KFold
training = asl.build_training(features_norm) # Experiment here with different feature sets
word = 'VEGETABLE' # Experiment here with different words
word_sequences = training.get_word_sequences(word)
split_method = KFold()
#print(word_sequences)
for cv_train_idx, cv_test_idx in split_method.split(word_sequences):
print("Train fold indices:{} Test fold indices:{}".format(cv_train_idx, cv_test_idx)) # view indices of the folds
Train fold indices:[2 3 4 5] Test fold indices:[0 1]
Train fold indices:[0 1 4 5] Test fold indices:[2 3]
Train fold indices:[0 1 2 3] Test fold indices:[4 5]
Tip: In order to run hmmlearn training using the X,lengths tuples on the new folds, subsets must be combined based on the indices given for the folds. A helper utility has been provided in the asl_utils module named combine_sequences for this purpose.
Scoring model topologies with BIC balances fit and complexity within the training set for each word. In the BIC equation, a penalty term penalizes complexity to avoid overfitting, so that it is not necessary to also use cross-validation in the selection process. There are a number of references on the internet for this criterion. These slides include a formula you may find helpful for your implementation.
The advantages of scoring model topologies with DIC over BIC are presented by Alain Biem in this reference (also found here). DIC scores the discriminant ability of a training set for one word against competing words. Instead of a penalty term for complexity, it provides a penalty if model liklihoods for non-matching words are too similar to model likelihoods for the correct word in the word set.
Implement SelectorCV, SelectorBIC, and SelectorDIC classes in the my_model_selectors.py module. Run the selectors on the following five words. Then answer the questions about your results.
Tip: The hmmlearn library may not be able to train or score all models. Implement try/except contructs as necessary to eliminate non-viable models from consideration.
In [31]:
words_to_train = ['FISH', 'BOOK', 'VEGETABLE', 'FUTURE', 'JOHN']
import timeit
In [32]:
# TODO: Implement SelectorCV in my_model_selector.py
%load_ext autoreload
%autoreload 2
from my_model_selectors import SelectorCV
training = asl.build_training(features_ground) # Experiment here with different feature sets defined in part 1
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
for word in words_to_train:
start = timeit.default_timer()
model = SelectorCV(sequences, Xlengths, word,
min_n_components=2, max_n_components=15, random_state=14).select()
end = timeit.default_timer()-start
if model is not None:
print("Training complete for {} with {} states with time {} seconds".format(word, model.n_components, end))
else:
print("Training failed for {}".format(word))
Training complete for FISH with 11 states with time 0.7969264570128871 seconds
Training complete for BOOK with 6 states with time 5.766494025010616 seconds
Training complete for VEGETABLE with 2 states with time 2.2095283990056487 seconds
Training complete for FUTURE with 2 states with time 5.271939098005532 seconds
Training complete for JOHN with 12 states with time 60.04147908498999 seconds
In [33]:
# TODO: Implement SelectorBIC in module my_model_selectors.py
%load_ext autoreload
%autoreload 2
from my_model_selectors import SelectorBIC
training = asl.build_training(features_ground) # Experiment here with different feature sets defined in part 1
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
for word in words_to_train:
start = timeit.default_timer()
model = SelectorBIC(sequences, Xlengths, word,
min_n_components=2, max_n_components=15, random_state=14).select()
end = timeit.default_timer()-start
if model is not None:
print("Training complete for {} with {} states with time {} seconds".format(word, model.n_components, end))
else:
print("Training failed for {}".format(word))
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
Training complete for FISH with 5 states with time 0.5327585319901118 seconds
Training complete for BOOK with 8 states with time 2.9897695160034345 seconds
Training complete for VEGETABLE with 9 states with time 1.0576528430101462 seconds
Training complete for FUTURE with 9 states with time 3.1648682310042204 seconds
Training complete for JOHN with 13 states with time 27.1895528340101 seconds
In [34]:
# TODO: Implement SelectorDIC in module my_model_selectors.py
from my_model_selectors import SelectorDIC
training = asl.build_training(features_ground) # Experiment here with different feature sets defined in part 1
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
for word in words_to_train:
start = timeit.default_timer()
model = SelectorDIC(sequences, Xlengths, word,
min_n_components=2, max_n_components=15, random_state=14).select()
end = timeit.default_timer()-start
if model is not None:
print("Training complete for {} with {} states with time {} seconds".format(word, model.n_components, end))
else:
print("Training failed for {}".format(word))
# Training complete for FISH with 3 states with time 1.1686174780006695 seconds
# Training complete for BOOK with 15 states with time 5.680475574001321 seconds
# Training complete for VEGETABLE with 15 states with time 4.188679099002911 seconds
# Training complete for FUTURE with 15 states with time 5.388806481001666 seconds
# Training complete for JOHN with 15 states with time 28.78554731499753 seconds
Training complete for FISH with 3 states with time 1.3867159090004861 seconds
Training complete for BOOK with 15 states with time 5.57528004499909 seconds
Training complete for VEGETABLE with 15 states with time 4.427838026997051 seconds
Training complete for FUTURE with 15 states with time 6.084797979987343 seconds
Training complete for JOHN with 15 states with time 31.26430395198986 seconds
Question 2: Compare and contrast the possible advantages and disadvantages of the various model selectors implemented.
Answer 2:
SelectorCV uses Cross Validation folds to split the data set into multiple different training and testing sets, then averaging the log likelihood of all sets. The result is a more accurate idea of how the model will perform on unseen data than simply checking the same data used to build the model. Its time to calculate scales with the number of KFolds necessary, while too many folds could potentially fail to reward good generalization.
BIC or Bayesian information criterion, penalizes models with complexity in particular the number of parameters to approximate the posterior probabilities. In addition it is much faster to calculate since the formula only requires variables which are already known.
DIC or Deviance information criterion, attempts to rate models based on the difference between their log likelihood scores and the average score across the anti evidence. By maximising the evidence and minimising the anti evidence the model chosen should be accurate and have a low rate of false positives. Depending on the size of the data set, calculating the anti evidence can be quite time consuming.
In [35]:
from asl_test_model_selectors import TestSelectors
suite = unittest.TestLoader().loadTestsFromModule(TestSelectors())
unittest.TextTestRunner().run(suite)
....
----------------------------------------------------------------------
Ran 4 tests in 72.687s
OK
Out[35]:
<unittest.runner.TextTestResult run=4 errors=0 failures=0>
The objective of this section is to "put it all together". Using the four feature sets created and the three model selectors, you will experiment with the models and present your results. Instead of training only five specific words as in the previous section, train the entire set with a feature set and model selector strategy.
The following example trains the entire set with the example features_ground and SelectorConstant features and model selector. Use this pattern for you experimentation and final submission cells.
In [36]:
# autoreload for automatically reloading changes made in my_model_selectors and my_recognizer
%load_ext autoreload
%autoreload 2
from my_model_selectors import SelectorConstant
def train_all_words(features, model_selector):
training = asl.build_training(features) # Experiment here with different feature sets defined in part 1
sequences = training.get_all_sequences()
Xlengths = training.get_all_Xlengths()
model_dict = {}
for word in training.words:
model = model_selector(sequences, Xlengths, word,
n_constant=3).select()
model_dict[word]=model
return model_dict
models = train_all_words(features_ground, SelectorConstant)
print("Number of word models returned = {}".format(len(models)))
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
Number of word models returned = 112
The build_test method in ASLdb is similar to the build_training method already presented, but there are a few differences:
SinglesData get_all_sequences, get_all_Xlengths, get_item_sequences and get_item_Xlengths
In [37]:
test_set = asl.build_test(features_ground)
print("Number of test set items: {}".format(test_set.num_items))
print("Number of test set sentences: {}".format(len(test_set.sentences_index)))
Number of test set items: 178
Number of test set sentences: 40
For the final project submission, students must implement a recognizer following guidance in the my_recognizer.py module. Experiment with the four feature sets and the three model selection methods (that's 12 possible combinations). You can add and remove cells for experimentation or run the recognizers locally in some other way during your experiments, but retain the results for your discussion. For submission, you will provide code cells of only three interesting combinations for your discussion (see questions below). At least one of these should produce a word error rate of less than 60%, i.e. WER < 0.60 .
Tip: The hmmlearn library may not be able to train or score all models. Implement try/except contructs as necessary to eliminate non-viable models from consideration.
In [38]:
%load_ext autoreload
%autoreload 2
# TODO implement the recognize method in my_recognizer
from my_recognizer import recognize
from asl_utils import show_errors
# TODO Choose a feature set and model selector
features = features_ground # change as needed
model_selector = SelectorConstant # change as needed
# TODO Recognize the test set and display the result with the show_errors method
models = train_all_words(features, model_selector)
test_set = asl.build_test(features)
probabilities, guesses = recognize(models, test_set)
show_errors(guesses, test_set)
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
**** WER = 0.6685393258426966
Total correct: 59 out of 178
Video Recognized Correct
=====================================================================================================
2: *GO WRITE *ARRIVE JOHN WRITE HOMEWORK
7: *SOMETHING-ONE *GO1 *IX CAN JOHN CAN GO CAN
12: JOHN *HAVE *WHAT CAN JOHN CAN GO CAN
21: JOHN *HOMEWORK *NEW *PREFER *CAR *CAR *FUTURE *EAT JOHN FISH WONT EAT BUT CAN EAT CHICKEN
25: *FRANK *TELL *LOVE *TELL *LOVE JOHN LIKE IX IX IX
28: *FRANK *TELL *LOVE *TELL *LOVE JOHN LIKE IX IX IX
30: *SHOULD LIKE *GO *GO *GO JOHN LIKE IX IX IX
36: *VISIT VEGETABLE *YESTERDAY *GIVE *MARY *MARY MARY VEGETABLE KNOW IX LIKE CORN1
40: *SUE *GIVE *CORN *VEGETABLE *GO JOHN IX THINK MARY LOVE
43: *FRANK *GO BUY HOUSE JOHN MUST BUY HOUSE
50: *FRANK *SEE BUY CAR *SOMETHING-ONE FUTURE JOHN BUY CAR SHOULD
54: JOHN SHOULD *WHO BUY HOUSE JOHN SHOULD NOT BUY HOUSE
57: *MARY *VISIT VISIT *VISIT JOHN DECIDE VISIT MARY
67: *LIKE FUTURE NOT BUY HOUSE JOHN FUTURE NOT BUY HOUSE
71: JOHN *FINISH VISIT MARY JOHN WILL VISIT MARY
74: *IX *VISIT *GO *GO JOHN NOT VISIT MARY
77: *JOHN BLAME *LOVE ANN BLAME MARY
84: *LOVE *ARRIVE *HOMEWORK BOOK IX-1P FIND SOMETHING-ONE BOOK
89: *GIVE *GIVE GIVE *IX IX *ARRIVE *BOOK JOHN IX GIVE MAN IX NEW COAT
90: *SOMETHING-ONE *SOMETHING-ONE IX *IX WOMAN *COAT JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
92: *FRANK GIVE *WOMAN *WOMAN WOMAN BOOK JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
100: POSS NEW CAR BREAK-DOWN POSS NEW CAR BREAK-DOWN
105: *FRANK *VEGETABLE JOHN LEG
107: *SHOULD *IX FRIEND *GO *JANA JOHN POSS FRIEND HAVE CANDY
108: *GIVE *LOVE WOMAN ARRIVE
113: IX CAR *CAR *IX *IX IX CAR BLUE SUE BUY
119: *PREFER *BUY1 IX *BLAME *IX SUE BUY IX CAR BLUE
122: JOHN *GIVE1 *COAT JOHN READ BOOK
139: *SHOULD *BUY1 *CAR *BLAME BOOK JOHN BUY WHAT YESTERDAY BOOK
142: *FRANK *STUDENT YESTERDAY *TEACHER BOOK JOHN BUY YESTERDAY WHAT BOOK
158: LOVE *MARY WHO LOVE JOHN WHO
167: *MARY IX *VISIT *WOMAN *LOVE JOHN IX SAY LOVE MARY
171: *VISIT *VISIT BLAME JOHN MARY BLAME
174: *CAN *GIVE3 GIVE1 *APPLE *WHAT PEOPLE GROUP GIVE1 JANA TOY
181: *BLAME ARRIVE JOHN ARRIVE
184: *GIVE1 BOY *GIVE1 TEACHER APPLE ALL BOY GIVE TEACHER APPLE
189: *JANA *SOMETHING-ONE *YESTERDAY *WHAT JOHN GIVE GIRL BOX
193: JOHN *SOMETHING-ONE *YESTERDAY BOX JOHN GIVE GIRL BOX
199: *LOVE CHOCOLATE WHO LIKE CHOCOLATE WHO
201: JOHN *GIVE *GIVE *LOVE *ARRIVE HOUSE JOHN TELL MARY IX-1P BUY HOUSE
In [39]:
# TODO Choose a feature set and model selector
# TODO Recognize the test set and display the result with the show_errors method
all_features = [features_ground, features_norm, features_polar, features_delta]
names_features = ['features_ground', 'features_norm', 'features_polar', 'features_delta']
model_selectors = [SelectorCV, SelectorBIC, SelectorDIC]
selector_names = ['SelectorCV', 'SelectorBIC', 'SelectorDIC']
# TODO Recognize the test set and display the result with the show_errors method
for feature_idx, feature_set in enumerate(all_features):
for selector_idx, model_selector in enumerate(model_selectors):
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'{names_features[feature_idx]} {selector_names[selector_idx]} in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
features_ground SelectorCV in 160.0786484489945 seconds
**** WER = 0.5337078651685393
Total correct: 83 out of 178
features_ground SelectorBIC in 90.93588310699852 seconds
**** WER = 0.550561797752809
Total correct: 80 out of 178
features_ground SelectorDIC in 270.5412421469955 seconds
**** WER = 0.5730337078651685
Total correct: 76 out of 178
features_norm SelectorCV in 199.50034927300294 seconds
**** WER = 0.5898876404494382
Total correct: 73 out of 178
features_norm SelectorBIC in 106.90185184100119 seconds
**** WER = 0.6123595505617978
Total correct: 69 out of 178
features_norm SelectorDIC in 273.304984153001 seconds
**** WER = 0.5955056179775281
Total correct: 72 out of 178
features_polar SelectorCV in 176.5529682769993 seconds
**** WER = 0.5674157303370787
Total correct: 77 out of 178
features_polar SelectorBIC in 104.54679734699312 seconds
**** WER = 0.5449438202247191
Total correct: 81 out of 178
features_polar SelectorDIC in 275.5706356860028 seconds
**** WER = 0.5449438202247191
Total correct: 81 out of 178
features_delta SelectorCV in 206.98176634300034 seconds
**** WER = 0.6348314606741573
Total correct: 65 out of 178
features_delta SelectorBIC in 126.23377896100283 seconds
**** WER = 0.601123595505618
Total correct: 71 out of 178
features_delta SelectorDIC in 271.3687613820075 seconds
**** WER = 0.6235955056179775
Total correct: 67 out of 178
In [40]:
# TODO Choose a feature set and model selector
# TODO Recognize the test set and display the result with the show_errors method
# features_ground SelectorCV in 163.83773794700028 seconds
# **** WER = 0.5337078651685393
# Total correct: 83 out of 178
# features_ground SelectorBIC in 92.56377709500157 seconds
# **** WER = 0.550561797752809
# Total correct: 80 out of 178
# features_ground SelectorDIC in 257.53987565600255 seconds
# **** WER = 0.5730337078651685
# Total correct: 76 out of 178
# features_norm SelectorCV in 179.7795833759992 seconds
# **** WER = 0.5898876404494382
# Total correct: 73 out of 178
# features_norm SelectorBIC in 89.84669804299847 seconds
# **** WER = 0.6123595505617978
# Total correct: 69 out of 178
# features_norm SelectorDIC in 282.46542226900056 seconds
# **** WER = 0.5955056179775281
# Total correct: 72 out of 178
# features_polar SelectorCV in 181.45353307999903 seconds
# **** WER = 0.5674157303370787
# Total correct: 77 out of 178
# features_polar SelectorBIC in 104.71394099800091 seconds
# **** WER = 0.5449438202247191
# Total correct: 81 out of 178
# features_polar SelectorDIC in 284.3083667409992 seconds
# **** WER = 0.5449438202247191
# Total correct: 81 out of 178
# features_delta SelectorCV in 235.1471823969987 seconds
# **** WER = 0.6348314606741573
# Total correct: 65 out of 178
# features_delta SelectorBIC in 129.87165026300136 seconds
# **** WER = 0.601123595505618
# Total correct: 71 out of 178
# features_delta SelectorDIC in 286.7942691110002 seconds
# **** WER = 0.6235955056179775
# Total correct: 67 out of 178
In [41]:
new_features = [features_scale, features_delta_scale, features_polar_scale, features_polar_norm, features_polar_scale_diff, features_polar_scale_diff_delta]
names_features = ['features_scale', 'features_delta_scale', 'features_polar_scale', 'features_polar_norm', 'features_polar_scale_diff', 'features_polar_scale_diff_delta']
model_selectors = [SelectorCV, SelectorBIC, SelectorDIC]
selector_names = ['SelectorCV', 'SelectorBIC', 'SelectorDIC']
# TODO Recognize the test set and display the result with the show_errors method
for feature_idx, feature_set in enumerate(new_features):
for selector_idx, model_selector in enumerate(model_selectors):
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'{names_features[feature_idx]} {selector_names[selector_idx]} in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
In [42]:
# features_scale SelectorCV in 179.69220569800018 seconds
# **** WER = 0.5674157303370787
# Total correct: 77 out of 178
# features_scale SelectorBIC in 92.94431864400008 seconds
# **** WER = 0.5842696629213483
# Total correct: 74 out of 178
# features_scale SelectorDIC in 268.32906491100016 seconds
# **** WER = 0.5561797752808989
# Total correct: 79 out of 178
# features_delta_scale SelectorCV in 165.2451469680002 seconds
# **** WER = 0.5168539325842697
# Total correct: 86 out of 178
# features_delta_scale SelectorBIC in 93.55489072 seconds
# **** WER = 0.5449438202247191
# Total correct: 81 out of 178
# features_delta_scale SelectorDIC in 267.83853616500005 seconds
# **** WER = 0.5449438202247191
# Total correct: 81 out of 178
# features_polar_scale SelectorCV in 18.832313753999642 seconds
# **** WER = 0.8314606741573034
# Total correct: 30 out of 178
# features_polar_scale SelectorBIC in 15.488588455000354 seconds
# **** WER = 0.8314606741573034
# Total correct: 30 out of 178
# features_polar_scale SelectorDIC in 15.453935098999864 seconds
# **** WER = 0.8314606741573034
# Total correct: 30 out of 178
# features_polar_norm SelectorCV in 177.13014139300003 seconds
# **** WER = 0.6179775280898876
# Total correct: 68 out of 178
# features_polar_norm SelectorBIC in 97.28063895800005 seconds
# **** WER = 0.5955056179775281
# Total correct: 72 out of 178
# features_polar_norm SelectorDIC in 275.7535377100003 seconds
# **** WER = 0.5730337078651685
# Total correct: 76 out of 178
# features_polar_scale_diff SelectorCV in 222.99059110300004 seconds
# **** WER = 0.7247191011235955
# Total correct: 49 out of 178
# features_polar_scale_diff SelectorBIC in 107.95954800999971 seconds
# **** WER = 0.7640449438202247
# Total correct: 42 out of 178
# features_polar_scale_diff SelectorDIC in 281.6946648809999 seconds
# **** WER = 0.7078651685393258
# Total correct: 52 out of 178
# features_polar_scale_diff_delta SelectorCV in 252.3127657199998 seconds
# **** WER = 0.6685393258426966
# Total correct: 59 out of 178
# features_polar_scale_diff_delta SelectorBIC in 126.35159485399981 seconds
# **** WER = 0.7078651685393258
# Total correct: 52 out of 178
# features_polar_scale_diff_delta SelectorDIC in 300.50953822600013 seconds
# **** WER = 0.6235955056179775
# Total correct: 67 out of 178
In [43]:
feature_set = features_ground + features_norm + features_polar + features_delta
model_selector = SelectorCV
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'4 features with SelectorCV in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
4 features with SelectorCV in 192.85549431599793 seconds
**** WER = 0.5337078651685393
Total correct: 83 out of 178
In [45]:
feature_set = features_ground + features_norm + features_polar + features_delta
model_selector = SelectorBIC
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'4 features with SelectorBIC in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
4 features with SelectorBIC in 126.27898304098926 seconds
**** WER = 0.47752808988764045
Total correct: 93 out of 178
In [46]:
feature_set = features_ground + features_norm + features_polar + features_delta
model_selector = SelectorDIC
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'4 features with SelectorDIC in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
4 features with SelectorDIC in 267.41922565200366 seconds
**** WER = 0.5280898876404494
Total correct: 84 out of 178
In [47]:
feature_set = features_scale
model_selector = SelectorBIC
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'features_scale with SelectorBIC in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
features_scale with SelectorBIC in 87.1936401839921 seconds
**** WER = 0.5842696629213483
Total correct: 74 out of 178
In [48]:
feature_set = features_ground + features_norm + features_polar + features_delta + features_scale + features_delta_scale + features_polar_scale + features_polar_norm + features_polar_scale_diff + features_polar_scale_diff_delta
model_selector = SelectorBIC
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'all features with SelectorBIC in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
all features with SelectorBIC in 102.64179495700228 seconds
**** WER = 0.8314606741573034
Total correct: 30 out of 178
In [49]:
feature_set = features_polar_norm + features_delta_scale + features_scale
model_selector = SelectorBIC
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'three new features with SelectorBIC in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
three new features with SelectorBIC in 108.47613012700458 seconds
**** WER = 0.5393258426966292
Total correct: 82 out of 178
In [50]:
feature_set = features_ground + features_norm + features_polar + features_delta_scale
# features_delta_scale is better than features_delta
model_selector = SelectorBIC
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'4 features with SelectorBIC in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
# ground, norm, polar, delta_scale
# **** WER = 0.4550561797752809
# Total correct: 97 out of 178
# ground, norm, polar_norm, delta_scale
# **** WER = 0.4943820224719101
# Total correct: 90 out of 178
4 features with SelectorBIC in 109.62327036699571 seconds
**** WER = 0.4550561797752809
Total correct: 97 out of 178
In [51]:
feature_set = features_ground + features_norm + features_polar + features_delta_scale
model_selectors = [SelectorCV, SelectorBIC, SelectorDIC]
selector_names = ['SelectorCV', 'SelectorBIC', 'SelectorDIC']
# TODO Recognize the test set and display the result with the show_errors method
for selector_idx, model_selector in enumerate(model_selectors):
model_selector = model_selectors[selector_idx]
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'4 features {selector_names[selector_idx]} in {end} seconds')
show_errors(guesses, test_set, short=True)
print('\n')
4 features SelectorCV in 171.38144103401282 seconds
**** WER = 0.47752808988764045
Total correct: 93 out of 178
4 features SelectorBIC in 110.029319475987 seconds
**** WER = 0.4550561797752809
Total correct: 97 out of 178
4 features SelectorDIC in 259.7781580699957 seconds
**** WER = 0.4887640449438202
Total correct: 91 out of 178
In [52]:
feature_set = features_ground + features_norm + features_polar + features_delta_scale
model_selectors = [SelectorBIC]
selector_names = ['SelectorBIC']
# TODO Recognize the test set and display the result with the show_errors method
for selector_idx, model_selector in enumerate(model_selectors):
model_selector = model_selectors[selector_idx]
start = timeit.default_timer()
models = train_all_words(feature_set, model_selector)
test_set = asl.build_test(feature_set)
probabilities, guesses = recognize(models, test_set)
end = timeit.default_timer() - start
print(f'4 best features {selector_names[selector_idx]} in {end} seconds')
show_errors(guesses, test_set, short=False)
print('\n')
4 best features SelectorBIC in 110.53751238899713 seconds
**** WER = 0.4550561797752809
Total correct: 97 out of 178
Video Recognized Correct
=====================================================================================================
2: JOHN WRITE *ARRIVE JOHN WRITE HOMEWORK
7: JOHN *PEOPLE GO CAN JOHN CAN GO CAN
12: JOHN *WHAT *WHAT CAN JOHN CAN GO CAN
21: JOHN *NEW *JOHN *MARY *CAR *CAR *FUTURE *FUTURE JOHN FISH WONT EAT BUT CAN EAT CHICKEN
25: JOHN *IX *LOVE IX IX JOHN LIKE IX IX IX
28: JOHN *MARY IX IX IX JOHN LIKE IX IX IX
30: JOHN LIKE IX IX IX JOHN LIKE IX IX IX
36: MARY *VISIT *GIVE IX *GO *MARY MARY VEGETABLE KNOW IX LIKE CORN1
40: JOHN IX *CORN MARY *IX JOHN IX THINK MARY LOVE
43: JOHN *JOHN BUY HOUSE JOHN MUST BUY HOUSE
50: *JOHN *SEE BUY CAR *JOHN FUTURE JOHN BUY CAR SHOULD
54: JOHN SHOULD *MARY BUY HOUSE JOHN SHOULD NOT BUY HOUSE
57: *IX *MARY *GIVE *IX JOHN DECIDE VISIT MARY
67: JOHN FUTURE *MARY BUY HOUSE JOHN FUTURE NOT BUY HOUSE
71: JOHN *FUTURE *BLAME MARY JOHN WILL VISIT MARY
74: *IX *VISIT *GIVE MARY JOHN NOT VISIT MARY
77: *JOHN BLAME MARY ANN BLAME MARY
84: *MARY *BUY *VISIT BOOK IX-1P FIND SOMETHING-ONE BOOK
89: *MARY IX *WOMAN *IX IX NEW COAT JOHN IX GIVE MAN IX NEW COAT
90: JOHN *IX IX *IX WOMAN BOOK JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
92: JOHN *WOMAN IX *IX WOMAN BOOK JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
100: POSS NEW CAR BREAK-DOWN POSS NEW CAR BREAK-DOWN
105: JOHN *SEE JOHN LEG
107: JOHN *JOHN FRIEND *GO *JOHN JOHN POSS FRIEND HAVE CANDY
108: *IX *LOVE WOMAN ARRIVE
113: IX CAR *IX *MARY *BOX IX CAR BLUE SUE BUY
119: *MARY *BUY1 IX *BLAME *GO SUE BUY IX CAR BLUE
122: JOHN *GIVE1 BOOK JOHN READ BOOK
139: JOHN *BUY1 WHAT *BOX BOOK JOHN BUY WHAT YESTERDAY BOOK
142: JOHN BUY YESTERDAY WHAT BOOK JOHN BUY YESTERDAY WHAT BOOK
158: LOVE JOHN WHO LOVE JOHN WHO
167: JOHN IX *VISIT LOVE MARY JOHN IX SAY LOVE MARY
171: JOHN *JOHN BLAME JOHN MARY BLAME
174: *JOHN *GIVE1 GIVE1 *GIVE *BLAME PEOPLE GROUP GIVE1 JANA TOY
181: JOHN *BOX JOHN ARRIVE
184: *IX BOY *GIVE1 TEACHER APPLE ALL BOY GIVE TEACHER APPLE
189: *MARY *IX *GIVE BOX JOHN GIVE GIRL BOX
193: JOHN *JOHN *GIVE BOX JOHN GIVE GIRL BOX
199: *JOHN CHOCOLATE *MARY LIKE CHOCOLATE WHO
201: JOHN *MARY *WOMAN *WOMAN BUY HOUSE JOHN TELL MARY IX-1P BUY HOUSE
In [ ]:
Question 3: Summarize the error results from three combinations of features and model selectors. What was the "best" combination and why? What additional information might we use to improve our WER? For more insight on improving WER, take a look at the introduction to Part 4.
Answer 3: I tested the combination of all three selectors and the original four features. The WER scores ranged from 0.533 to 0.634, with the best scores found with SelectorCV or SelectorBIC. The best single original feature seemed to be the features_ground, followed by the features_polar. When it came to selector speed, the SelectorBIC was approximately two times faster than SelectorCV and up to three times faster than SelectorDIC.
While testing feature combinations and new features I mostly used the SelectorBIC because of its good and fast results.
Next I tried to combine several features together to see what impact that had on the WER score.
All four original features together with the SelectorBIC provided the best score so far which was 0.477.
I then tested all my additional custom features looking to see if any of them alone were effective.
The two best were features_delta_scale with SelectorCV at 0.516 WER and features_scale with SelectorDIC at 0.556 WER.
I initially tried to add some of these new features to the existing four but the scores always suffered. Perhaps this was the BIC selector feature complexity penalty coming into affect, or simply that the features were muddying the difference between words.
Once I realised that adding features wasn't going to help, I decided to try substituting features with some of the new ones I had created. In the end I saw a minor improvement by swapping features_delta with features_delta_scale getting the score down to 0.455 WER.
The final combination was: features_ground + features_norm + features_polar + features_delta_scale
Interestingly delta_scale is not a scaled version of the features_delta which is based on the features_ground, but rather its based on features_scale which is a scaling of the original left and right x,y values.
Other substitutions didn't seem to yield beneficial results.
Finding the best features could be improved by automating the process of checking different combinations and comparing them after processing is done. Alternatively something like PCA (principal component analysis) might assist in determining which features are contributing to the accuracy the most.
In [53]:
from asl_test_recognizer import TestRecognize
suite = unittest.TestLoader().loadTestsFromModule(TestRecognize())
unittest.TextTestRunner().run(suite)
..
----------------------------------------------------------------------
Ran 2 tests in 39.245s
OK
Out[53]:
<unittest.runner.TextTestResult run=2 errors=0 failures=0>
We've squeezed just about as much as we can out of the model and still only get about 50% of the words right! Surely we can do better than that. Probability to the rescue again in the form of statistical language models (SLM). The basic idea is that each word has some probability of occurrence within the set, and some probability that it is adjacent to specific other words. We can use that additional information to make better choices.
The recognizer you implemented in Part 3 is equivalent to a "0-gram" SLM. Improve the WER with the SLM data provided with the data set in the link above using "1-gram", "2-gram", and/or "3-gram" statistics. The probabilities data you've already calculated will be useful and can be turned into a pandas DataFrame if desired (see next cell).
Good luck! Share your results with the class!
In [54]:
# create a DataFrame of log likelihoods for the test word items
df_probs = pd.DataFrame(data=probabilities)
df_probs.head()
Out[54]:
ALL
ANN
APPLE
ARRIVE
BILL
BLAME
BLUE
BOOK
BORROW
BOX
...
VIDEOTAPE
VISIT
WANT
WHAT
WHO
WILL
WOMAN
WONT
WRITE
YESTERDAY
0
-7705.601616
-5.956598e+05
-6050.334288
-879.761332
-36323.480984
-759.621705
-11222.660812
-1879.023493
-25222.330412
-1149.220569
...
-4155.593113
-660.121962
-2751.840456
-1567.083050
-655.410092
-8218.631951
-1690.212925
-1407.056670
-536.991853
-651.603534
1
-19259.175028
-5.845157e+05
-9228.351886
-293.672909
-243970.374961
-381.847665
-10396.935054
-236.532384
-9951.875492
-834.368943
...
-657.674342
-1155.145961
-3279.514911
-1159.474768
-928.622184
-18949.933699
-754.981055
-1415.752239
-117.251162
-1596.370111
2
-28580.301709
-2.448877e+06
-17228.875082
-657.251168
-348651.625977
-938.663882
-24041.100096
-1734.484502
-13665.878804
-1583.254267
...
-3386.531589
-1645.438520
-5453.116562
-2492.247993
-2984.947584
-29081.914437
-2316.330873
-2452.393357
-1315.298038
-1942.403497
3
-3785.711028
-5.542399e+06
-1751.824407
-660.954839
-5029.213466
-974.146128
-2748.053099
-2631.339665
-118841.373786
-1574.867975
...
-7823.838506
-451.702956
-7505.190362
-1992.976187
-2375.235791
-36714.621985
-613.779745
-1647.251542
-3803.117669
-1465.255973
4
-6148.626998
-2.657050e+06
-1952.022313
-169.375252
-44839.230884
-163.031271
-573.838102
-333.114123
-3900.199403
-125.412826
...
-538.412989
-152.447444
-1006.299072
-185.830550
-2371.739105
-17336.167841
-414.729404
-1137.616987
-1333.199825
-589.186707
5 rows × 112 columns
In [68]:
def scale_probabilities(probabilities):
clean_probabilties = dict((word,score) for word, score in probabilities.items() if score > float('-inf'))
scores = [score for word, score in clean_probabilties.items()]
minimum = min(scores)
maximum = max(scores)
diff = maximum - minimum
# norm = (original value - mean) / standard deviation
norm_probabilities = {}
for word, score in clean_probabilties.items():
norm_probabilities[word] = (clean_probabilties[word] - minimum) / diff
return norm_probabilities
In [69]:
def normalize_probabilities(probabilities):
# standard score (z score) = (X - μ) / σ
# calculate mean
clean_probabilties = dict((word,score) for word, score in probabilities.items() if score > float('-inf'))
scores = [score for word, score in clean_probabilties.items()]
mean = np.mean(scores)
std = np.std(scores)
# calculate standard score
norm_probabilities = {}
for word, score in clean_probabilties.items():
norm_probabilities[word] = (clean_probabilties[word] - mean) / std
return norm_probabilities
In [107]:
sentence_list = [[idx] * len(test_set.sentences_index[idx]) for idx in test_set.sentences_index]
print(len(sentence_list))
print(sentence_list)
40
[[2, 2, 2], [7, 7, 7, 7], [12, 12, 12, 12], [21, 21, 21, 21, 21, 21, 21, 21], [25, 25, 25, 25, 25], [28, 28, 28, 28, 28], [30, 30, 30, 30, 30], [36, 36, 36, 36, 36, 36], [40, 40, 40, 40, 40], [43, 43, 43, 43], [50, 50, 50, 50, 50], [54, 54, 54, 54, 54], [57, 57, 57, 57], [67, 67, 67, 67, 67], [71, 71, 71, 71], [74, 74, 74, 74], [77, 77, 77], [84, 84, 84, 84], [89, 89, 89, 89, 89, 89, 89], [90, 90, 90, 90, 90, 90], [92, 92, 92, 92, 92, 92], [100, 100, 100, 100], [105, 105], [107, 107, 107, 107, 107], [108, 108], [113, 113, 113, 113, 113], [119, 119, 119, 119, 119], [122, 122, 122], [139, 139, 139, 139, 139], [142, 142, 142, 142, 142], [158, 158, 158], [167, 167, 167, 167, 167], [171, 171, 171], [174, 174, 174, 174, 174], [181, 181], [184, 184, 184, 184, 184], [189, 189, 189, 189], [193, 193, 193, 193], [199, 199, 199], [201, 201, 201, 201, 201, 201]]
In [112]:
%load_ext autoreload
%autoreload 2
import arpa
import kenlm
import itertools
three_arpa_models = arpa.loadf('data/ukn.3.lm')
lm_three = three_arpa_models[0] # ARPA files may contain several models.
master_idx = -1
new_guesses = []
verbose = True
for sidx, sentence_words in enumerate(sentence_list):
start = timeit.default_timer()
# if sidx < 2:
if True:
sentence_probs = []
sentence_candidates = []
sentence_scores = []
for idx, video_id in enumerate(sentence_words):
master_idx += 1
old_probs = probabilities[master_idx]
norm_probs = normalize_probabilities(old_probs)
scale_probs = scale_probabilities(old_probs)
candidates = [(score, word) for word, score in old_probs.items()]
candidates = sorted(candidates)[::-1]
old_best_score, old_word = candidates[0]
cut_off = old_best_score * 3
candidates = [(score, word) for score, word in candidates if score >= cut_off]
candidates = candidates[:num_best]
candidates = [word.rstrip('123456789') for score, word in candidates]
candidates = candidates[:3] # keep no more than 3
if verbose:
print(f'{idx} candidates {candidates}')
sentence_probs.append((norm_probs, scale_probs, old_probs))
if len(candidates) < 1:
candidates.append(old_word)
sentence_candidates.append(candidates)
combinations = itertools.product(*sentence_candidates)
num_sentences = 0
for sentence in combinations:
num_sentences += 1
org_logl = 0
lm_score = 0
norm_score = 0
for i in range(len(sentence)):
word = sentence[i]
norm_score += sentence_probs[i][0][word]
# org_logl += sentence_probs[i][2][word]
# lm_score += lm_three.log_p(sentence[:i + 1])
# lm_score += lm_three.log_s(sentence[:i + 1])
lm_score += lm_three.log_s(sentence)
new_score = (norm_score / len(sentence)) + lm_score * 10
if verbose:
print(f'sentence {sentence} {org_logl} {lm_score} {new_score}')
sentence_scores.append((new_score, sentence, org_logl, lm_score))
best_sentence = max(sentence_scores)
if verbose:
print(f'Best sentence {best_sentence}')
print('\n\n')
new_guesses += best_sentence[1]
end = timeit.default_timer() - start
if verbose:
print(f'num_sentences {num_sentences} {end} seconds')
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
0 candidates ['JOHN', 'NOT', 'FUTURE']
1 candidates ['WRITE', 'BOOK', 'BROTHER']
2 candidates ['ARRIVE', 'JOHN', 'NEW']
sentence ('JOHN', 'WRITE', 'ARRIVE') 0 -4.999848099999994 -49.8118874121372
sentence ('JOHN', 'WRITE', 'JOHN') 0 -5.598988200000008 -55.80329030663219
sentence ('JOHN', 'WRITE', 'NEW') 0 -6.304562899999993 -62.85903848939415
sentence ('JOHN', 'BOOK', 'ARRIVE') 0 -5.363856599999991 -53.451976712239684
sentence ('JOHN', 'BOOK', 'JOHN') 0 -5.962996700000005 -59.44337960673467
sentence ('JOHN', 'BOOK', 'NEW') 0 -6.6685713999999905 -66.49912778949664
sentence ('JOHN', 'BROTHER', 'ARRIVE') 0 -3.01913506999999 -30.0047614633173
sentence ('JOHN', 'BROTHER', 'JOHN') 0 -5.828014199999998 -58.093554657812234
sentence ('JOHN', 'BROTHER', 'NEW') 0 -6.533588899999998 -65.14930284057434
sentence ('NOT', 'WRITE', 'ARRIVE') 0 -7.788773999999989 -77.70116196675934
sentence ('NOT', 'WRITE', 'JOHN') 0 -8.387914100000003 -83.69256486125433
sentence ('NOT', 'WRITE', 'NEW') 0 -9.093488799999989 -90.74831304401629
sentence ('NOT', 'BOOK', 'ARRIVE') 0 -7.406482799999992 -73.87825426686187
sentence ('NOT', 'BOOK', 'JOHN') 0 -8.005622900000006 -79.86965716135687
sentence ('NOT', 'BOOK', 'NEW') 0 -8.711197599999991 -86.92540534411883
sentence ('NOT', 'BROTHER', 'ARRIVE') 0 -5.5778459699999985 -55.59188601793957
sentence ('NOT', 'BROTHER', 'JOHN') 0 -8.386725100000007 -83.6806792124345
sentence ('NOT', 'BROTHER', 'NEW') 0 -9.092299800000006 -90.7364273951966
sentence ('FUTURE', 'WRITE', 'ARRIVE') 0 -7.449977999999987 -74.31320448129884
sentence ('FUTURE', 'WRITE', 'JOHN') 0 -8.049118100000001 -80.30460737579384
sentence ('FUTURE', 'WRITE', 'NEW') 0 -8.754692799999987 -87.3603555585558
sentence ('FUTURE', 'BOOK', 'ARRIVE') 0 -7.06768679999999 -70.49029678140138
sentence ('FUTURE', 'BOOK', 'JOHN') 0 -7.666826900000004 -76.48169967589637
sentence ('FUTURE', 'BOOK', 'NEW') 0 -8.37240159999999 -83.53744785865833
sentence ('FUTURE', 'BROTHER', 'ARRIVE') 0 -5.239049969999982 -52.203928532478926
sentence ('FUTURE', 'BROTHER', 'JOHN') 0 -8.04792909999999 -80.29272172697387
sentence ('FUTURE', 'BROTHER', 'NEW') 0 -8.75350379999999 -87.34846990973597
Best sentence (-30.004761463317301, ('JOHN', 'BROTHER', 'ARRIVE'), 0, -3.01913506999999)
num_sentences 27 0.02464433800196275 seconds
0 candidates ['JOHN', 'MARY', 'IX']
1 candidates ['PEOPLE', 'BOX', 'TEACHER']
2 candidates ['GO', 'JOHN', 'IX']
3 candidates ['CAN', 'ARRIVE', 'BOX']
sentence ('JOHN', 'PEOPLE', 'GO', 'CAN') 0 -6.447134739999996 -64.24694023484314
sentence ('JOHN', 'PEOPLE', 'GO', 'ARRIVE') 0 -7.314711700000004 -72.9227223686685
sentence ('JOHN', 'PEOPLE', 'GO', 'BOX') 0 -7.696474800000004 -76.74035606107819
sentence ('JOHN', 'PEOPLE', 'JOHN', 'CAN') 0 -7.614232399999992 -75.9179321946497
sentence ('JOHN', 'PEOPLE', 'JOHN', 'ARRIVE') 0 -6.563947999999996 -65.41510072847504
sentence ('JOHN', 'PEOPLE', 'JOHN', 'BOX') 0 -6.733564240000007 -67.11126582088482
sentence ('JOHN', 'PEOPLE', 'IX', 'CAN') 0 -7.377048099999996 -73.54609061078018
sentence ('JOHN', 'PEOPLE', 'IX', 'ARRIVE') 0 -6.912682899999993 -68.90245114460544
sentence ('JOHN', 'PEOPLE', 'IX', 'BOX') 0 -7.294446000000008 -72.72008483701526
sentence ('JOHN', 'BOX', 'GO', 'CAN') 0 -7.139027339999998 -71.16587011839398
sentence ('JOHN', 'BOX', 'GO', 'ARRIVE') 0 -8.006604300000006 -79.84165225221936
sentence ('JOHN', 'BOX', 'GO', 'BOX') 0 -8.388367400000007 -83.65928594462905
sentence ('JOHN', 'BOX', 'JOHN', 'CAN') 0 -8.306124999999994 -82.83686207820055
sentence ('JOHN', 'BOX', 'JOHN', 'ARRIVE') 0 -7.255840599999999 -72.3340306120259
sentence ('JOHN', 'BOX', 'JOHN', 'BOX') 0 -7.42545684000001 -74.03019570443567
sentence ('JOHN', 'BOX', 'IX', 'CAN') 0 -8.068940699999999 -80.46502049433103
sentence ('JOHN', 'BOX', 'IX', 'ARRIVE') 0 -7.604575499999996 -75.8213810281563
sentence ('JOHN', 'BOX', 'IX', 'BOX') 0 -7.9863386000000105 -79.63901472056612
sentence ('JOHN', 'TEACHER', 'GO', 'CAN') 0 -6.623622740000002 -66.01182486889186
sentence ('JOHN', 'TEACHER', 'GO', 'ARRIVE') 0 -7.49119970000001 -74.68760700271723
sentence ('JOHN', 'TEACHER', 'GO', 'BOX') 0 -7.8729628000000105 -78.50524069512691
sentence ('JOHN', 'TEACHER', 'JOHN', 'CAN') 0 -7.790720399999998 -77.68281682869842
sentence ('JOHN', 'TEACHER', 'JOHN', 'ARRIVE') 0 -6.7404360000000025 -67.17998536252377
sentence ('JOHN', 'TEACHER', 'JOHN', 'BOX') 0 -6.910052240000013 -68.87615045493355
sentence ('JOHN', 'TEACHER', 'IX', 'CAN') 0 -7.553536100000002 -75.3109752448289
sentence ('JOHN', 'TEACHER', 'IX', 'ARRIVE') 0 -7.089170899999999 -70.66733577865416
sentence ('JOHN', 'TEACHER', 'IX', 'BOX') 0 -7.470934000000014 -74.48496947106399
sentence ('MARY', 'PEOPLE', 'GO', 'CAN') 0 -7.682146340000003 -76.59706042878081
sentence ('MARY', 'PEOPLE', 'GO', 'ARRIVE') 0 -8.54972330000001 -85.27284256260617
sentence ('MARY', 'PEOPLE', 'GO', 'BOX') 0 -8.931486400000011 -89.09047625501586
sentence ('MARY', 'PEOPLE', 'JOHN', 'CAN') 0 -8.849243999999999 -88.26805238858736
sentence ('MARY', 'PEOPLE', 'JOHN', 'ARRIVE') 0 -7.798959600000003 -77.76522092241271
sentence ('MARY', 'PEOPLE', 'JOHN', 'BOX') 0 -7.968575840000014 -79.46138601482248
sentence ('MARY', 'PEOPLE', 'IX', 'CAN') 0 -8.612059700000003 -85.89621080471784
sentence ('MARY', 'PEOPLE', 'IX', 'ARRIVE') 0 -8.1476945 -81.2525713385431
sentence ('MARY', 'PEOPLE', 'IX', 'BOX') 0 -8.529457600000015 -85.07020503095293
sentence ('MARY', 'BOX', 'GO', 'CAN') 0 -8.159664340000006 -81.37224431233166
sentence ('MARY', 'BOX', 'GO', 'ARRIVE') 0 -9.027241300000014 -90.04802644615704
sentence ('MARY', 'BOX', 'GO', 'BOX') 0 -9.409004400000015 -93.86566013856671
sentence ('MARY', 'BOX', 'JOHN', 'CAN') 0 -9.326762000000002 -93.04323627213823
sentence ('MARY', 'BOX', 'JOHN', 'ARRIVE') 0 -8.276477600000007 -82.54040480596356
sentence ('MARY', 'BOX', 'JOHN', 'BOX') 0 -8.446093840000017 -84.23656989837335
sentence ('MARY', 'BOX', 'IX', 'CAN') 0 -9.089577700000007 -90.6713946882687
sentence ('MARY', 'BOX', 'IX', 'ARRIVE') 0 -8.625212500000004 -86.02775522209396
sentence ('MARY', 'BOX', 'IX', 'BOX') 0 -9.006975600000018 -89.84538891450379
sentence ('MARY', 'TEACHER', 'GO', 'CAN') 0 -7.858634340000009 -78.36194506282952
sentence ('MARY', 'TEACHER', 'GO', 'ARRIVE') 0 -8.726211300000017 -87.0377271966549
sentence ('MARY', 'TEACHER', 'GO', 'BOX') 0 -9.107974400000018 -90.85536088906458
sentence ('MARY', 'TEACHER', 'JOHN', 'CAN') 0 -9.025732000000005 -90.03293702263609
sentence ('MARY', 'TEACHER', 'JOHN', 'ARRIVE') 0 -7.97544760000001 -79.53010555646142
sentence ('MARY', 'TEACHER', 'JOHN', 'BOX') 0 -8.14506384000002 -81.2262706488712
sentence ('MARY', 'TEACHER', 'IX', 'CAN') 0 -8.78854770000001 -87.66109543876657
sentence ('MARY', 'TEACHER', 'IX', 'ARRIVE') 0 -8.324182500000006 -83.01745597259183
sentence ('MARY', 'TEACHER', 'IX', 'BOX') 0 -8.705945600000021 -86.83508966500165
sentence ('IX', 'PEOPLE', 'GO', 'CAN') 0 -7.178457739999985 -71.56017701868693
sentence ('IX', 'PEOPLE', 'GO', 'ARRIVE') 0 -8.046034699999993 -80.2359591525123
sentence ('IX', 'PEOPLE', 'GO', 'BOX') 0 -8.427797799999993 -84.05359284492198
sentence ('IX', 'PEOPLE', 'JOHN', 'CAN') 0 -8.34555539999998 -83.23116897849349
sentence ('IX', 'PEOPLE', 'JOHN', 'ARRIVE') 0 -7.295270999999985 -72.72833751231883
sentence ('IX', 'PEOPLE', 'JOHN', 'BOX') 0 -7.464887239999996 -74.4245026047286
sentence ('IX', 'PEOPLE', 'IX', 'CAN') 0 -8.108371099999985 -80.85932739462396
sentence ('IX', 'PEOPLE', 'IX', 'ARRIVE') 0 -7.644005899999982 -76.21568792844923
sentence ('IX', 'PEOPLE', 'IX', 'BOX') 0 -8.025768999999997 -80.03332162085906
sentence ('IX', 'BOX', 'GO', 'CAN') 0 -7.681322839999993 -76.58883190223783
sentence ('IX', 'BOX', 'GO', 'ARRIVE') 0 -8.548899800000001 -85.26461403606321
sentence ('IX', 'BOX', 'GO', 'BOX') 0 -8.930662900000002 -89.08224772847288
sentence ('IX', 'BOX', 'JOHN', 'CAN') 0 -8.848420499999989 -88.2598238620444
sentence ('IX', 'BOX', 'JOHN', 'ARRIVE') 0 -7.798136099999994 -77.75699239586973
sentence ('IX', 'BOX', 'JOHN', 'BOX') 0 -7.967752340000004 -79.45315748827952
sentence ('IX', 'BOX', 'IX', 'CAN') 0 -8.611236199999993 -85.88798227817487
sentence ('IX', 'BOX', 'IX', 'ARRIVE') 0 -8.14687099999999 -81.24434281200013
sentence ('IX', 'BOX', 'IX', 'BOX') 0 -8.528634100000005 -85.06197650440996
sentence ('IX', 'TEACHER', 'GO', 'CAN') 0 -7.380292839999996 -73.57853265273569
sentence ('IX', 'TEACHER', 'GO', 'ARRIVE') 0 -8.247869800000004 -82.25431478656107
sentence ('IX', 'TEACHER', 'GO', 'BOX') 0 -8.629632900000004 -86.07194847897075
sentence ('IX', 'TEACHER', 'JOHN', 'CAN') 0 -8.547390499999992 -85.24952461254226
sentence ('IX', 'TEACHER', 'JOHN', 'ARRIVE') 0 -7.497106099999996 -74.74669314636759
sentence ('IX', 'TEACHER', 'JOHN', 'BOX') 0 -7.666722340000007 -76.44285823877738
sentence ('IX', 'TEACHER', 'IX', 'CAN') 0 -8.310206199999996 -82.87768302867273
sentence ('IX', 'TEACHER', 'IX', 'ARRIVE') 0 -7.845840999999993 -78.234043562498
sentence ('IX', 'TEACHER', 'IX', 'BOX') 0 -8.227604100000008 -82.05167725490782
Best sentence (-64.246940234843137, ('JOHN', 'PEOPLE', 'GO', 'CAN'), 0, -6.447134739999996)
num_sentences 81 0.09510392899392173 seconds
0 candidates ['JOHN', 'IX', 'WOMAN']
1 candidates ['WHAT', 'CAR', 'HOUSE']
2 candidates ['WHAT', 'JOHN', 'GO']
3 candidates ['CAN', 'HOUSE', 'WHAT']
sentence ('JOHN', 'WHAT', 'WHAT', 'CAN') 0 -7.308358900000002 -72.8813848328861
sentence ('JOHN', 'WHAT', 'WHAT', 'HOUSE') 0 -7.680564599999997 -76.6034442100426
sentence ('JOHN', 'WHAT', 'WHAT', 'WHAT') 0 -6.705265499999996 -66.85045726472127
sentence ('JOHN', 'WHAT', 'JOHN', 'CAN') 0 -7.271029599999991 -72.50809217502837
sentence ('JOHN', 'WHAT', 'JOHN', 'HOUSE') 0 -7.488289600000002 -74.68069455218502
sentence ('JOHN', 'WHAT', 'JOHN', 'WHAT') 0 -6.512990500000001 -64.92770760686368
sentence ('JOHN', 'WHAT', 'GO', 'CAN') 0 -6.1296892399999905 -61.09473070129965
sentence ('JOHN', 'WHAT', 'GO', 'HOUSE') 0 -7.833837099999997 -78.13621167845626
sentence ('JOHN', 'WHAT', 'GO', 'WHAT') 0 -6.858537999999996 -68.38322473313494
sentence ('JOHN', 'CAR', 'WHAT', 'CAN') 0 -7.546228200000016 -75.26008036343305
sentence ('JOHN', 'CAR', 'WHAT', 'HOUSE') 0 -7.918433900000011 -78.98213974058953
sentence ('JOHN', 'CAR', 'WHAT', 'WHAT') 0 -6.94313480000001 -69.2291527952682
sentence ('JOHN', 'CAR', 'JOHN', 'CAN') 0 -7.534656200000001 -75.14436070557525
sentence ('JOHN', 'CAR', 'JOHN', 'HOUSE') 0 -7.751916200000011 -77.3169630827319
sentence ('JOHN', 'CAR', 'JOHN', 'WHAT') 0 -6.77661710000001 -67.56397613741058
sentence ('JOHN', 'CAR', 'GO', 'CAN') 0 -6.3675585400000045 -63.47342623184659
sentence ('JOHN', 'CAR', 'GO', 'HOUSE') 0 -8.07170640000001 -80.5149072090032
sentence ('JOHN', 'CAR', 'GO', 'WHAT') 0 -7.09640730000001 -70.76192026368187
sentence ('JOHN', 'HOUSE', 'WHAT', 'CAN') 0 -7.802292400000013 -77.8207229330602
sentence ('JOHN', 'HOUSE', 'WHAT', 'HOUSE') 0 -8.174498100000008 -81.5427823102167
sentence ('JOHN', 'HOUSE', 'WHAT', 'WHAT') 0 -7.199199000000007 -71.78979536489535
sentence ('JOHN', 'HOUSE', 'JOHN', 'CAN') 0 -7.790720399999998 -77.7050032752024
sentence ('JOHN', 'HOUSE', 'JOHN', 'HOUSE') 0 -8.007980400000008 -79.87760565235905
sentence ('JOHN', 'HOUSE', 'JOHN', 'WHAT') 0 -7.032681300000007 -70.12461870703773
sentence ('JOHN', 'HOUSE', 'GO', 'CAN') 0 -6.623622740000002 -66.03406880147374
sentence ('JOHN', 'HOUSE', 'GO', 'HOUSE') 0 -8.327770600000008 -83.07554977863035
sentence ('JOHN', 'HOUSE', 'GO', 'WHAT') 0 -7.352471500000007 -73.32256283330902
sentence ('IX', 'WHAT', 'WHAT', 'CAN') 0 -8.06502900000001 -80.44809319392448
sentence ('IX', 'WHAT', 'WHAT', 'HOUSE') 0 -8.437234700000005 -84.17015257108096
sentence ('IX', 'WHAT', 'WHAT', 'WHAT') 0 -7.461935600000004 -74.41716562575964
sentence ('IX', 'WHAT', 'JOHN', 'CAN') 0 -8.0276997 -80.07480053606673
sentence ('IX', 'WHAT', 'JOHN', 'HOUSE') 0 -8.24495970000001 -82.24740291322338
sentence ('IX', 'WHAT', 'JOHN', 'WHAT') 0 -7.269660600000009 -72.49441596790206
sentence ('IX', 'WHAT', 'GO', 'CAN') 0 -6.8863593399999985 -68.66143906233802
sentence ('IX', 'WHAT', 'GO', 'HOUSE') 0 -8.590507200000005 -85.70292003949463
sentence ('IX', 'WHAT', 'GO', 'WHAT') 0 -7.615208100000004 -75.9499330941733
sentence ('IX', 'CAR', 'WHAT', 'CAN') 0 -8.208797500000003 -81.8857807244712
sentence ('IX', 'CAR', 'WHAT', 'HOUSE') 0 -8.581003199999998 -85.60784010162769
sentence ('IX', 'CAR', 'WHAT', 'WHAT') 0 -7.605704099999997 -75.85485315630636
sentence ('IX', 'CAR', 'JOHN', 'CAN') 0 -8.197225499999988 -81.77006106661341
sentence ('IX', 'CAR', 'JOHN', 'HOUSE') 0 -8.414485499999998 -83.94266344377006
sentence ('IX', 'CAR', 'JOHN', 'WHAT') 0 -7.439186399999997 -74.18967649844873
sentence ('IX', 'CAR', 'GO', 'CAN') 0 -7.030127839999992 -70.09912659288474
sentence ('IX', 'CAR', 'GO', 'HOUSE') 0 -8.734275699999998 -87.14060757004135
sentence ('IX', 'CAR', 'GO', 'WHAT') 0 -7.758976599999997 -77.38762062472003
sentence ('IX', 'HOUSE', 'WHAT', 'CAN') 0 -8.558962500000007 -85.38743129409842
sentence ('IX', 'HOUSE', 'WHAT', 'HOUSE') 0 -8.931168200000002 -89.10949067125492
sentence ('IX', 'HOUSE', 'WHAT', 'WHAT') 0 -7.955869100000001 -79.35650372593359
sentence ('IX', 'HOUSE', 'JOHN', 'CAN') 0 -8.547390499999992 -85.27171163624064
sentence ('IX', 'HOUSE', 'JOHN', 'HOUSE') 0 -8.764650500000002 -87.44431401339727
sentence ('IX', 'HOUSE', 'JOHN', 'WHAT') 0 -7.789351400000001 -77.69132706807595
sentence ('IX', 'HOUSE', 'GO', 'CAN') 0 -7.380292839999996 -73.60077716251197
sentence ('IX', 'HOUSE', 'GO', 'HOUSE') 0 -9.084440700000002 -90.64225813966857
sentence ('IX', 'HOUSE', 'GO', 'WHAT') 0 -8.109141600000001 -80.88927119434724
sentence ('WOMAN', 'WHAT', 'WHAT', 'CAN') 0 -8.441718900000012 -84.21501059602804
sentence ('WOMAN', 'WHAT', 'WHAT', 'HOUSE') 0 -8.813924600000007 -87.93706997318453
sentence ('WOMAN', 'WHAT', 'WHAT', 'WHAT') 0 -7.838625500000006 -78.18408302786321
sentence ('WOMAN', 'WHAT', 'JOHN', 'CAN') 0 -8.404389600000002 -83.8417179381703
sentence ('WOMAN', 'WHAT', 'JOHN', 'HOUSE') 0 -8.621649600000012 -86.01432031532696
sentence ('WOMAN', 'WHAT', 'JOHN', 'WHAT') 0 -7.646350500000011 -76.26133337000563
sentence ('WOMAN', 'WHAT', 'GO', 'CAN') 0 -7.263049240000001 -72.42835646444159
sentence ('WOMAN', 'WHAT', 'GO', 'HOUSE') 0 -8.967197100000007 -89.4698374415982
sentence ('WOMAN', 'WHAT', 'GO', 'WHAT') 0 -7.991898000000006 -79.71685049627688
sentence ('WOMAN', 'CAR', 'WHAT', 'CAN') 0 -9.194965400000015 -91.74747812657488
sentence ('WOMAN', 'CAR', 'WHAT', 'HOUSE') 0 -9.56717110000001 -95.46953750373136
sentence ('WOMAN', 'CAR', 'WHAT', 'WHAT') 0 -8.59187200000001 -85.71655055841003
sentence ('WOMAN', 'CAR', 'JOHN', 'CAN') 0 -9.1833934 -91.63175846871708
sentence ('WOMAN', 'CAR', 'JOHN', 'HOUSE') 0 -9.40065340000001 -93.80436084587373
sentence ('WOMAN', 'CAR', 'JOHN', 'WHAT') 0 -8.42535430000001 -84.0513739005524
sentence ('WOMAN', 'CAR', 'GO', 'CAN') 0 -8.016295740000004 -79.96082399498842
sentence ('WOMAN', 'CAR', 'GO', 'HOUSE') 0 -9.72044360000001 -97.00230497214503
sentence ('WOMAN', 'CAR', 'GO', 'WHAT') 0 -8.74514450000001 -87.2493180268237
sentence ('WOMAN', 'HOUSE', 'WHAT', 'CAN') 0 -9.451029600000012 -94.30812069620202
sentence ('WOMAN', 'HOUSE', 'WHAT', 'HOUSE') 0 -9.823235300000007 -98.03018007335852
sentence ('WOMAN', 'HOUSE', 'WHAT', 'WHAT') 0 -8.847936200000007 -88.2771931280372
sentence ('WOMAN', 'HOUSE', 'JOHN', 'CAN') 0 -9.439457599999997 -94.19240103834423
sentence ('WOMAN', 'HOUSE', 'JOHN', 'HOUSE') 0 -9.656717600000007 -96.36500341550088
sentence ('WOMAN', 'HOUSE', 'JOHN', 'WHAT') 0 -8.681418500000007 -86.61201647017955
sentence ('WOMAN', 'HOUSE', 'GO', 'CAN') 0 -8.272359940000001 -82.52146656461558
sentence ('WOMAN', 'HOUSE', 'GO', 'HOUSE') 0 -9.976507800000007 -99.56294754177217
sentence ('WOMAN', 'HOUSE', 'GO', 'WHAT') 0 -9.001208700000007 -89.80996059645085
Best sentence (-61.094730701299653, ('JOHN', 'WHAT', 'GO', 'CAN'), 0, -6.1296892399999905)
num_sentences 81 0.08724048700241838 seconds
0 candidates ['JOHN', 'IX', 'MARY']
1 candidates ['NEW', 'JOHN', 'ARRIVE']
2 candidates ['JOHN', 'NEW', 'WONT']
3 candidates ['MARY', 'JOHN', 'NOT']
4 candidates ['CAR', 'NEW', 'STUDENT']
5 candidates ['CAR', 'WHAT', 'STUDENT']
6 candidates ['FUTURE', 'CAN', 'MARY']
7 candidates ['FUTURE', 'JOHN', 'MARY']
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'FUTURE', 'FUTURE') 0 -14.660790300000002 -146.42373639257892
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'FUTURE', 'JOHN') 0 -14.419187199999996 -144.0077060895838
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'FUTURE', 'MARY') 0 -14.121247100000005 -141.02830670571967
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'CAN', 'FUTURE') 0 -15.658222800000004 -156.39806313201964
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'CAN', 'JOHN') 0 -15.923552900000004 -159.05136482902455
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'CAN', 'MARY') 0 -15.118679600000007 -151.0026334451604
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'MARY', 'FUTURE') 0 -15.356797000000014 -153.38380516514243
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'MARY', 'JOHN') 0 -15.255162700000014 -152.36746286214736
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'CAR', 'MARY', 'MARY') 0 -14.817253800000017 -147.9883754782832
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'WHAT', 'FUTURE', 'FUTURE') 0 -15.420234500000007 -154.01817969709396
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'WHAT', 'FUTURE', 'JOHN') 0 -15.178631400000015 -151.60214939409897
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'WHAT', 'FUTURE', 'MARY') 0 -14.88069130000001 -148.6227500102347
sentence ('JOHN', 'NEW', 'JOHN', 'MARY', 'CAR', 'WHAT', 'CAN', 'FUTURE') 0 -15.420353500000019 -154.01937143653478
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num_sentences 6561 13.10860540199792 seconds
0 candidates ['JOHN', 'IX', 'MARY']
1 candidates ['IX', 'MARY', 'JOHN']
2 candidates ['LOVE', 'IX', 'MARY']
3 candidates ['IX', 'MARY', 'JOHN']
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Best sentence (-37.060203404482948, ('JOHN', 'IX', 'IX', 'IX', 'IX'), 0, -3.723011189999994)
num_sentences 243 0.31947377500182483 seconds
0 candidates ['JOHN', 'MARY', 'IX']
1 candidates ['MARY', 'IX', 'JOHN']
2 candidates ['IX', 'MARY', 'LOVE']
3 candidates ['IX', 'MARY', 'JOHN']
4 candidates ['IX', 'MARY', 'LOVE']
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sentence ('IX', 'JOHN', 'LOVE', 'IX', 'LOVE') 0 -8.561380900000003 -85.44656205843376
sentence ('IX', 'JOHN', 'LOVE', 'MARY', 'IX') 0 -7.699709099999993 -76.82983848028135
sentence ('IX', 'JOHN', 'LOVE', 'MARY', 'MARY') 0 -7.6782769 -76.61552223830383
sentence ('IX', 'JOHN', 'LOVE', 'MARY', 'LOVE') 0 -6.83211279999999 -68.1538855634622
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sentence ('IX', 'JOHN', 'LOVE', 'JOHN', 'MARY') 0 -8.1192085 -81.02483946002731
sentence ('IX', 'JOHN', 'LOVE', 'JOHN', 'LOVE') 0 -8.202005600000007 -81.85281478518586
Best sentence (-37.062833686147442, ('JOHN', 'IX', 'IX', 'IX', 'IX'), 0, -3.723011189999994)
num_sentences 243 0.2866654229874257 seconds
0 candidates ['JOHN', 'IX']
1 candidates []
2 candidates ['IX', 'LOVE', 'MARY']
3 candidates ['IX', 'MARY', 'GO']
4 candidates ['IX', 'LOVE', 'MARY']
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sentence ('IX', 'LIKE', 'IX', 'GO', 'IX') 0 -8.591194330000008 -85.71363037085601
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sentence ('IX', 'LIKE', 'MARY', 'MARY', 'LOVE') 0 -7.582861100000002 -75.63031946974503
sentence ('IX', 'LIKE', 'MARY', 'MARY', 'MARY') 0 -8.429025200000012 -84.09196499241006
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sentence ('IX', 'LIKE', 'MARY', 'GO', 'MARY') 0 -8.612232700000007 -85.92404306429691
Best sentence (-24.553754046546111, ('JOHN', 'LIKE', 'IX', 'IX', 'IX'), 0, -2.475207190000006)
num_sentences 54 0.06542859399633016 seconds
0 candidates ['MARY', 'JOHN', 'IX']
1 candidates ['VISIT', 'MARY', 'JOHN']
2 candidates ['GIVE', 'GIRL', 'IX']
3 candidates ['IX', 'GIVE', 'GO']
4 candidates ['GO', 'MARY', 'IX']
5 candidates ['MARY', 'JOHN', 'IX']
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sentence ('IX', 'JOHN', 'IX', 'GO', 'MARY', 'JOHN') 0 -10.890088590000005 -108.72427315258844
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sentence ('IX', 'JOHN', 'IX', 'GO', 'IX', 'IX') 0 -9.779916190000009 -97.62255252143785
Best sentence (-58.269390030303796, ('JOHN', 'MARY', 'IX', 'IX', 'IX', 'IX'), 0, -5.8446010000000115)
num_sentences 729 1.0782808280055178 seconds
0 candidates ['JOHN', 'MARY']
1 candidates ['IX', 'JOHN', 'VISIT']
2 candidates []
3 candidates []
4 candidates ['IX', 'MARY', 'LOVE']
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sentence ('JOHN', 'IX', 'CORN', 'MARY', 'MARY') 0 -8.214548390000004 -81.97603642709095
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sentence ('MARY', 'JOHN', 'CORN', 'MARY', 'LOVE') 0 -9.152652799999998 -91.35709707119936
sentence ('MARY', 'VISIT', 'CORN', 'MARY', 'IX') 0 -10.249663400000017 -102.32720118692278
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sentence ('MARY', 'VISIT', 'CORN', 'MARY', 'LOVE') 0 -9.382067100000015 -93.65124484509603
Best sentence (-73.514400184769059, ('JOHN', 'IX', 'CORN', 'MARY', 'LOVE'), 0, -7.3683842899999945)
num_sentences 18 0.023523766998550855 seconds
0 candidates ['JOHN']
1 candidates ['JOHN', 'IX', 'FUTURE']
2 candidates ['BUY', 'STUDENT', 'NEW']
3 candidates ['HOUSE', 'CAR', 'STUDENT']
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sentence ('JOHN', 'IX', 'NEW', 'STUDENT') 0 -7.244829190000004 -72.26444593715694
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sentence ('JOHN', 'FUTURE', 'BUY', 'CAR') 0 -5.352172499999995 -53.33787682041789
sentence ('JOHN', 'FUTURE', 'BUY', 'STUDENT') 0 -7.011855100000005 -69.93470334079197
sentence ('JOHN', 'FUTURE', 'STUDENT', 'HOUSE') 0 -7.505601900000002 -74.87216786317572
sentence ('JOHN', 'FUTURE', 'STUDENT', 'CAR') 0 -7.501437100000004 -74.83052487965035
sentence ('JOHN', 'FUTURE', 'STUDENT', 'STUDENT') 0 -8.150431100000006 -81.32046540002435
sentence ('JOHN', 'FUTURE', 'NEW', 'HOUSE') 0 -7.3289165999999994 -73.10531620131974
sentence ('JOHN', 'FUTURE', 'NEW', 'CAR') 0 -6.315414500000003 -62.97030021779438
sentence ('JOHN', 'FUTURE', 'NEW', 'STUDENT') 0 -7.973745800000003 -79.55361373816837
Best sentence (-35.146665103943235, ('JOHN', 'FUTURE', 'BUY', 'HOUSE'), 0, -3.533051829999991)
num_sentences 27 0.02871886099455878 seconds
0 candidates ['JOHN', 'IX', 'IX-1P']
1 candidates ['SEE']
2 candidates ['BUY', 'WRITE', 'NEW']
3 candidates ['CAR', 'WHAT', 'HAVE']
4 candidates ['JOHN', 'NEW', 'IX']
sentence ('JOHN', 'SEE', 'BUY', 'CAR', 'JOHN') 0 -7.98672590000001 -79.68270557147288
sentence ('JOHN', 'SEE', 'BUY', 'CAR', 'NEW') 0 -8.692300599999996 -86.73845815312944
sentence ('JOHN', 'SEE', 'BUY', 'CAR', 'IX') 0 -7.203284800000006 -71.84830661480187
sentence ('JOHN', 'SEE', 'BUY', 'WHAT', 'JOHN') 0 -8.553430300000002 -85.34975171412759
sentence ('JOHN', 'SEE', 'BUY', 'WHAT', 'NEW') 0 -9.284762299999997 -92.66307729578425
sentence ('JOHN', 'SEE', 'BUY', 'WHAT', 'IX') 0 -7.795746500000007 -77.77292575745668
sentence ('JOHN', 'SEE', 'BUY', 'HAVE', 'JOHN') 0 -9.253082000000006 -92.34627059384638
sentence ('JOHN', 'SEE', 'BUY', 'HAVE', 'NEW') 0 -9.958656700000006 -99.40202317550309
sentence ('JOHN', 'SEE', 'BUY', 'HAVE', 'IX') 0 -8.469640900000002 -84.51187163717537
sentence ('JOHN', 'SEE', 'WRITE', 'CAR', 'JOHN') 0 -9.581308000000007 -95.62852784049302
sentence ('JOHN', 'SEE', 'WRITE', 'CAR', 'NEW') 0 -10.286882699999992 -102.68428042214958
sentence ('JOHN', 'SEE', 'WRITE', 'CAR', 'IX') 0 -8.797866900000002 -87.794128883822
sentence ('JOHN', 'SEE', 'WRITE', 'WHAT', 'JOHN') 0 -9.317681400000012 -92.99226398314786
sentence ('JOHN', 'SEE', 'WRITE', 'WHAT', 'NEW') 0 -10.049013400000007 -100.30558956480452
sentence ('JOHN', 'SEE', 'WRITE', 'WHAT', 'IX') 0 -8.559997600000003 -85.41543802647679
sentence ('JOHN', 'SEE', 'WRITE', 'HAVE', 'JOHN') 0 -9.836975500000008 -98.18520686286658
sentence ('JOHN', 'SEE', 'WRITE', 'HAVE', 'NEW') 0 -10.542550200000008 -105.24095944452328
sentence ('JOHN', 'SEE', 'WRITE', 'HAVE', 'IX') 0 -9.053534400000004 -90.35080790619556
sentence ('JOHN', 'SEE', 'NEW', 'CAR', 'JOHN') 0 -8.269157700000008 -82.50702485939895
sentence ('JOHN', 'SEE', 'NEW', 'CAR', 'NEW') 0 -8.974732400000008 -89.56277744105566
sentence ('JOHN', 'SEE', 'NEW', 'CAR', 'IX') 0 -7.4857166000000035 -74.67262590272794
sentence ('JOHN', 'SEE', 'NEW', 'WHAT', 'JOHN') 0 -9.014868400000012 -89.96413400205378
sentence ('JOHN', 'SEE', 'NEW', 'WHAT', 'NEW') 0 -9.746200400000006 -97.27745958371044
sentence ('JOHN', 'SEE', 'NEW', 'WHAT', 'IX') 0 -8.257184600000002 -82.38730804538272
sentence ('JOHN', 'SEE', 'NEW', 'HAVE', 'JOHN') 0 -9.534162500000008 -95.1570768817725
sentence ('JOHN', 'SEE', 'NEW', 'HAVE', 'NEW') 0 -10.239737200000008 -102.2128294634292
sentence ('JOHN', 'SEE', 'NEW', 'HAVE', 'IX') 0 -8.750721400000003 -87.32267792510149
sentence ('IX', 'SEE', 'BUY', 'CAR', 'JOHN') 0 -9.850687499999992 -98.32233073897002
sentence ('IX', 'SEE', 'BUY', 'CAR', 'NEW') 0 -10.556262199999978 -105.37808332062659
sentence ('IX', 'SEE', 'BUY', 'CAR', 'IX') 0 -9.067246399999988 -90.48793178229901
sentence ('IX', 'SEE', 'BUY', 'WHAT', 'JOHN') 0 -10.417391899999984 -103.98937688162474
sentence ('IX', 'SEE', 'BUY', 'WHAT', 'NEW') 0 -11.148723899999979 -111.30270246328139
sentence ('IX', 'SEE', 'BUY', 'WHAT', 'IX') 0 -9.659708099999989 -96.41255092495382
sentence ('IX', 'SEE', 'BUY', 'HAVE', 'JOHN') 0 -11.117043599999988 -110.98589576134353
sentence ('IX', 'SEE', 'BUY', 'HAVE', 'NEW') 0 -11.822618299999988 -118.04164834300023
sentence ('IX', 'SEE', 'BUY', 'HAVE', 'IX') 0 -10.333602499999984 -103.1514968046725
sentence ('IX', 'SEE', 'WRITE', 'CAR', 'JOHN') 0 -11.445269600000003 -114.2681530079903
sentence ('IX', 'SEE', 'WRITE', 'CAR', 'NEW') 0 -12.150844299999989 -121.32390558964687
sentence ('IX', 'SEE', 'WRITE', 'CAR', 'IX') 0 -10.661828499999999 -106.43375405131928
sentence ('IX', 'SEE', 'WRITE', 'WHAT', 'JOHN') 0 -11.181643000000008 -111.63188915064515
sentence ('IX', 'SEE', 'WRITE', 'WHAT', 'NEW') 0 -11.912975000000003 -118.9452147323018
sentence ('IX', 'SEE', 'WRITE', 'WHAT', 'IX') 0 -10.423959199999999 -104.05506319397408
sentence ('IX', 'SEE', 'WRITE', 'HAVE', 'JOHN') 0 -11.700937100000004 -116.82483203036386
sentence ('IX', 'SEE', 'WRITE', 'HAVE', 'NEW') 0 -12.406511800000004 -123.88058461202057
sentence ('IX', 'SEE', 'WRITE', 'HAVE', 'IX') 0 -10.917496 -108.99043307369284
sentence ('IX', 'SEE', 'NEW', 'CAR', 'JOHN') 0 -10.13311929999999 -101.1466500268961
sentence ('IX', 'SEE', 'NEW', 'CAR', 'NEW') 0 -10.83869399999999 -108.2024026085528
sentence ('IX', 'SEE', 'NEW', 'CAR', 'IX') 0 -9.349678199999985 -93.31225107022509
sentence ('IX', 'SEE', 'NEW', 'WHAT', 'JOHN') 0 -10.878829999999994 -108.60375916955093
sentence ('IX', 'SEE', 'NEW', 'WHAT', 'NEW') 0 -11.610161999999988 -115.91708475120758
sentence ('IX', 'SEE', 'NEW', 'WHAT', 'IX') 0 -10.121146199999984 -101.02693321287987
sentence ('IX', 'SEE', 'NEW', 'HAVE', 'JOHN') 0 -11.39812409999999 -113.79670204926964
sentence ('IX', 'SEE', 'NEW', 'HAVE', 'NEW') 0 -12.10369879999999 -120.85245463092635
sentence ('IX', 'SEE', 'NEW', 'HAVE', 'IX') 0 -10.614682999999985 -105.96230309259863
sentence ('IX-1P', 'SEE', 'BUY', 'CAR', 'JOHN') 0 -8.569414899999998 -85.50960894131597
sentence ('IX-1P', 'SEE', 'BUY', 'CAR', 'NEW') 0 -9.274989599999984 -92.56536152297254
sentence ('IX-1P', 'SEE', 'BUY', 'CAR', 'IX') 0 -7.785973799999994 -77.67520998464495
sentence ('IX-1P', 'SEE', 'BUY', 'WHAT', 'JOHN') 0 -9.13611929999999 -91.17665508397069
sentence ('IX-1P', 'SEE', 'BUY', 'WHAT', 'NEW') 0 -9.867451299999985 -98.48998066562734
sentence ('IX-1P', 'SEE', 'BUY', 'WHAT', 'IX') 0 -8.378435499999995 -83.59982912729976
sentence ('IX-1P', 'SEE', 'BUY', 'HAVE', 'JOHN') 0 -9.835770999999994 -98.17317396368948
sentence ('IX-1P', 'SEE', 'BUY', 'HAVE', 'NEW') 0 -10.541345699999994 -105.22892654534617
sentence ('IX-1P', 'SEE', 'BUY', 'HAVE', 'IX') 0 -9.05232989999999 -90.33877500701846
sentence ('IX-1P', 'SEE', 'WRITE', 'CAR', 'JOHN') 0 -10.163997000000009 -101.45543121033624
sentence ('IX-1P', 'SEE', 'WRITE', 'CAR', 'NEW') 0 -10.869571699999995 -108.5111837919928
sentence ('IX-1P', 'SEE', 'WRITE', 'CAR', 'IX') 0 -9.380555900000004 -93.62103225366523
sentence ('IX-1P', 'SEE', 'WRITE', 'WHAT', 'JOHN') 0 -9.900370400000014 -98.81916735299109
sentence ('IX-1P', 'SEE', 'WRITE', 'WHAT', 'NEW') 0 -10.631702400000009 -106.13249293464774
sentence ('IX-1P', 'SEE', 'WRITE', 'WHAT', 'IX') 0 -9.142686600000005 -91.24234139632003
sentence ('IX-1P', 'SEE', 'WRITE', 'HAVE', 'JOHN') 0 -10.41966450000001 -104.0121102327098
sentence ('IX-1P', 'SEE', 'WRITE', 'HAVE', 'NEW') 0 -11.12523920000001 -111.0678628143665
sentence ('IX-1P', 'SEE', 'WRITE', 'HAVE', 'IX') 0 -9.636223400000006 -96.17771127603879
sentence ('IX-1P', 'SEE', 'NEW', 'CAR', 'JOHN') 0 -8.851846699999996 -88.33392822924205
sentence ('IX-1P', 'SEE', 'NEW', 'CAR', 'NEW') 0 -9.557421399999996 -95.38968081089875
sentence ('IX-1P', 'SEE', 'NEW', 'CAR', 'IX') 0 -8.068405599999991 -80.49952927257102
sentence ('IX-1P', 'SEE', 'NEW', 'WHAT', 'JOHN') 0 -9.5975574 -95.79103737189688
sentence ('IX-1P', 'SEE', 'NEW', 'WHAT', 'NEW') 0 -10.328889399999994 -103.10436295355353
sentence ('IX-1P', 'SEE', 'NEW', 'WHAT', 'IX') 0 -8.83987359999999 -88.2142114152258
sentence ('IX-1P', 'SEE', 'NEW', 'HAVE', 'JOHN') 0 -10.116851499999996 -100.98398025161559
sentence ('IX-1P', 'SEE', 'NEW', 'HAVE', 'NEW') 0 -10.822426199999995 -108.0397328332723
sentence ('IX-1P', 'SEE', 'NEW', 'HAVE', 'IX') 0 -9.333410399999991 -93.14958129494457
Best sentence (-71.848306614801871, ('JOHN', 'SEE', 'BUY', 'CAR', 'IX'), 0, -7.203284800000006)
num_sentences 81 0.09586423900327645 seconds
0 candidates ['JOHN', 'SOMETHING-ONE', 'LOVE']
1 candidates ['SHOULD']
2 candidates ['MARY', 'NOT', 'FUTURE']
3 candidates ['BUY', 'NEW', 'ARRIVE']
4 candidates ['HOUSE', 'TEACHER']
sentence ('JOHN', 'SHOULD', 'MARY', 'BUY', 'HOUSE') 0 -5.927627529999995 -59.06396930240019
sentence ('JOHN', 'SHOULD', 'MARY', 'BUY', 'TEACHER') 0 -9.2303395 -92.09109476891723
sentence ('JOHN', 'SHOULD', 'MARY', 'NEW', 'HOUSE') 0 -9.042682099999993 -90.2145193086177
sentence ('JOHN', 'SHOULD', 'MARY', 'NEW', 'TEACHER') 0 -9.511419999999987 -94.90190407513462
sentence ('JOHN', 'SHOULD', 'MARY', 'ARRIVE', 'HOUSE') 0 -8.879657800000004 -88.5842764977301
sentence ('JOHN', 'SHOULD', 'MARY', 'ARRIVE', 'TEACHER') 0 -9.348395699999998 -93.27166126424703
sentence ('JOHN', 'SHOULD', 'NOT', 'BUY', 'HOUSE') 0 -2.122419199999996 -21.011890531166944
sentence ('JOHN', 'SHOULD', 'NOT', 'BUY', 'TEACHER') 0 -6.749868800000002 -67.286392297684
sentence ('JOHN', 'SHOULD', 'NOT', 'NEW', 'HOUSE') 0 -7.534595800000005 -75.13366083738457
sentence ('JOHN', 'SHOULD', 'NOT', 'NEW', 'TEACHER') 0 -8.003333699999999 -79.8210456039015
sentence ('JOHN', 'SHOULD', 'NOT', 'ARRIVE', 'HOUSE') 0 -7.371571500000016 -73.50341802649697
sentence ('JOHN', 'SHOULD', 'NOT', 'ARRIVE', 'TEACHER') 0 -7.8403094000000095 -78.1908027930139
sentence ('JOHN', 'SHOULD', 'FUTURE', 'BUY', 'HOUSE') 0 -5.548124129999991 -55.26894138078206
sentence ('JOHN', 'SHOULD', 'FUTURE', 'BUY', 'TEACHER') 0 -8.850836099999995 -88.2960668472991
sentence ('JOHN', 'SHOULD', 'FUTURE', 'NEW', 'HOUSE') 0 -9.3439889 -93.22759338699967
sentence ('JOHN', 'SHOULD', 'FUTURE', 'NEW', 'TEACHER') 0 -9.812726799999993 -97.91497815351661
sentence ('JOHN', 'SHOULD', 'FUTURE', 'ARRIVE', 'HOUSE') 0 -9.18096460000001 -91.59735057611208
sentence ('JOHN', 'SHOULD', 'FUTURE', 'ARRIVE', 'TEACHER') 0 -9.649702500000004 -96.28473534262902
sentence ('SOMETHING-ONE', 'SHOULD', 'MARY', 'BUY', 'HOUSE') 0 -8.083852629999996 -80.62625859847591
sentence ('SOMETHING-ONE', 'SHOULD', 'MARY', 'BUY', 'TEACHER') 0 -11.3865646 -113.65338406499296
sentence ('SOMETHING-ONE', 'SHOULD', 'MARY', 'NEW', 'HOUSE') 0 -11.198907199999994 -111.77680860469343
sentence ('SOMETHING-ONE', 'SHOULD', 'MARY', 'NEW', 'TEACHER') 0 -11.667645099999987 -116.46419337121036
sentence ('SOMETHING-ONE', 'SHOULD', 'MARY', 'ARRIVE', 'HOUSE') 0 -11.035882900000004 -110.14656579380583
sentence ('SOMETHING-ONE', 'SHOULD', 'MARY', 'ARRIVE', 'TEACHER') 0 -11.504620799999998 -114.83395056032276
sentence ('SOMETHING-ONE', 'SHOULD', 'NOT', 'BUY', 'HOUSE') 0 -5.45337099999999 -54.32144682724262
sentence ('SOMETHING-ONE', 'SHOULD', 'NOT', 'BUY', 'TEACHER') 0 -10.080820599999996 -100.59594859375967
sentence ('SOMETHING-ONE', 'SHOULD', 'NOT', 'NEW', 'HOUSE') 0 -10.8655476 -108.44321713346024
sentence ('SOMETHING-ONE', 'SHOULD', 'NOT', 'NEW', 'TEACHER') 0 -11.334285499999993 -113.13060189997717
sentence ('SOMETHING-ONE', 'SHOULD', 'NOT', 'ARRIVE', 'HOUSE') 0 -10.70252330000001 -106.81297432257264
sentence ('SOMETHING-ONE', 'SHOULD', 'NOT', 'ARRIVE', 'TEACHER') 0 -11.171261200000004 -111.50035908908957
sentence ('SOMETHING-ONE', 'SHOULD', 'FUTURE', 'BUY', 'HOUSE') 0 -7.704349229999991 -76.8312306768578
sentence ('SOMETHING-ONE', 'SHOULD', 'FUTURE', 'BUY', 'TEACHER') 0 -11.007061199999995 -109.85835614337483
sentence ('SOMETHING-ONE', 'SHOULD', 'FUTURE', 'NEW', 'HOUSE') 0 -11.500214 -114.7898826830754
sentence ('SOMETHING-ONE', 'SHOULD', 'FUTURE', 'NEW', 'TEACHER') 0 -11.968951899999993 -119.47726744959235
sentence ('SOMETHING-ONE', 'SHOULD', 'FUTURE', 'ARRIVE', 'HOUSE') 0 -11.33718970000001 -113.15963987218781
sentence ('SOMETHING-ONE', 'SHOULD', 'FUTURE', 'ARRIVE', 'TEACHER') 0 -11.805927600000004 -117.84702463870475
sentence ('LOVE', 'SHOULD', 'MARY', 'BUY', 'HOUSE') 0 -8.72911243 -87.0788637936967
sentence ('LOVE', 'SHOULD', 'MARY', 'BUY', 'TEACHER') 0 -12.031824400000005 -120.10598926021373
sentence ('LOVE', 'SHOULD', 'MARY', 'NEW', 'HOUSE') 0 -11.844166999999999 -118.2294137999142
sentence ('LOVE', 'SHOULD', 'MARY', 'NEW', 'TEACHER') 0 -12.312904899999992 -122.91679856643113
sentence ('LOVE', 'SHOULD', 'MARY', 'ARRIVE', 'HOUSE') 0 -11.68114270000001 -116.5991709890266
sentence ('LOVE', 'SHOULD', 'MARY', 'ARRIVE', 'TEACHER') 0 -12.149880600000003 -121.28655575554355
sentence ('LOVE', 'SHOULD', 'NOT', 'BUY', 'HOUSE') 0 -6.098630799999995 -60.77405202246339
sentence ('LOVE', 'SHOULD', 'NOT', 'BUY', 'TEACHER') 0 -10.7260804 -107.04855378898044
sentence ('LOVE', 'SHOULD', 'NOT', 'NEW', 'HOUSE') 0 -11.510807400000004 -114.895822328681
sentence ('LOVE', 'SHOULD', 'NOT', 'NEW', 'TEACHER') 0 -11.979545299999998 -119.58320709519793
sentence ('LOVE', 'SHOULD', 'NOT', 'ARRIVE', 'HOUSE') 0 -11.347783100000015 -113.26557951779341
sentence ('LOVE', 'SHOULD', 'NOT', 'ARRIVE', 'TEACHER') 0 -11.816521000000009 -117.95296428431034
sentence ('LOVE', 'SHOULD', 'FUTURE', 'BUY', 'HOUSE') 0 -8.349609029999996 -83.28383587207857
sentence ('LOVE', 'SHOULD', 'FUTURE', 'BUY', 'TEACHER') 0 -11.652321 -116.3109613385956
sentence ('LOVE', 'SHOULD', 'FUTURE', 'NEW', 'HOUSE') 0 -12.145473800000005 -121.24248787829617
sentence ('LOVE', 'SHOULD', 'FUTURE', 'NEW', 'TEACHER') 0 -12.614211699999998 -125.92987264481312
sentence ('LOVE', 'SHOULD', 'FUTURE', 'ARRIVE', 'HOUSE') 0 -11.982449500000016 -119.61224506740858
sentence ('LOVE', 'SHOULD', 'FUTURE', 'ARRIVE', 'TEACHER') 0 -12.45118740000001 -124.29962983392552
Best sentence (-21.011890531166944, ('JOHN', 'SHOULD', 'NOT', 'BUY', 'HOUSE'), 0, -2.122419199999996)
num_sentences 54 0.06868025299627334 seconds
0 candidates ['IX', 'MARY', 'JOHN']
1 candidates ['MARY', 'JOHN', 'IX']
2 candidates ['GIVE', 'VISIT', 'IX']
3 candidates ['IX', 'LOVE', 'VISIT']
sentence ('IX', 'MARY', 'GIVE', 'IX') 0 -7.056813099999999 -70.377313348312
sentence ('IX', 'MARY', 'GIVE', 'LOVE') 0 -8.278042100000008 -82.58962208454571
sentence ('IX', 'MARY', 'GIVE', 'VISIT') 0 -8.728439900000012 -87.0936011444011
sentence ('IX', 'MARY', 'VISIT', 'IX') 0 -7.082360000000008 -70.63278311083616
sentence ('IX', 'MARY', 'VISIT', 'LOVE') 0 -7.886697100000006 -78.67617284706978
sentence ('IX', 'MARY', 'VISIT', 'VISIT') 0 -8.337094900000011 -83.18015190692518
sentence ('IX', 'MARY', 'IX', 'IX') 0 -6.463397000000015 -64.44315684437102
sentence ('IX', 'MARY', 'IX', 'LOVE') 0 -7.942008600000008 -79.2292915806046
sentence ('IX', 'MARY', 'IX', 'VISIT') 0 -8.392406400000013 -83.73327064045999
sentence ('IX', 'JOHN', 'GIVE', 'IX') 0 -5.874623999999997 -58.555425126722355
sentence ('IX', 'JOHN', 'GIVE', 'LOVE') 0 -8.446064800000002 -84.26985186295603
sentence ('IX', 'JOHN', 'GIVE', 'VISIT') 0 -8.896462600000007 -88.77383092281144
sentence ('IX', 'JOHN', 'VISIT', 'IX') 0 -6.667903900000013 -66.48822488924658
sentence ('IX', 'JOHN', 'VISIT', 'LOVE') 0 -7.472241000000011 -74.5316146254802
sentence ('IX', 'JOHN', 'VISIT', 'VISIT') 0 -7.922638800000016 -79.0355936853356
sentence ('IX', 'JOHN', 'IX', 'IX') 0 -5.888003089999998 -58.689220522781234
sentence ('IX', 'JOHN', 'IX', 'LOVE') 0 -7.366614689999992 -73.4753552590148
sentence ('IX', 'JOHN', 'IX', 'VISIT') 0 -7.817012489999996 -77.9793343188702
sentence ('IX', 'IX', 'GIVE', 'IX') 0 -6.9208777000000055 -69.01797276577831
sentence ('IX', 'IX', 'GIVE', 'LOVE') 0 -8.142106700000014 -81.23028150201203
sentence ('IX', 'IX', 'GIVE', 'VISIT') 0 -8.592504500000018 -85.73426056186743
sentence ('IX', 'IX', 'VISIT', 'IX') 0 -7.82100410000001 -78.01923752830244
sentence ('IX', 'IX', 'VISIT', 'LOVE') 0 -8.625341200000008 -86.06262726453606
sentence ('IX', 'IX', 'VISIT', 'VISIT') 0 -9.075739000000013 -90.56660632439146
sentence ('IX', 'IX', 'IX', 'IX') 0 -3.6684218999999985 -36.49341926183712
sentence ('IX', 'IX', 'IX', 'LOVE') 0 -6.8294494000000014 -68.10371299807078
sentence ('IX', 'IX', 'IX', 'VISIT') 0 -7.279847200000006 -72.60769205792619
sentence ('MARY', 'MARY', 'GIVE', 'IX') 0 -7.5351545999999985 -75.16073235537608
sentence ('MARY', 'MARY', 'GIVE', 'LOVE') 0 -8.756383600000007 -87.37304109160979
sentence ('MARY', 'MARY', 'GIVE', 'VISIT') 0 -9.206781400000011 -91.87702015146519
sentence ('MARY', 'MARY', 'VISIT', 'IX') 0 -7.5607015000000075 -75.41620211790024
sentence ('MARY', 'MARY', 'VISIT', 'LOVE') 0 -8.365038600000005 -83.45959185413386
sentence ('MARY', 'MARY', 'VISIT', 'VISIT') 0 -8.81543640000001 -87.96357091398926
sentence ('MARY', 'MARY', 'IX', 'IX') 0 -6.941738500000014 -69.2265758514351
sentence ('MARY', 'MARY', 'IX', 'LOVE') 0 -8.420350100000007 -84.01271058766866
sentence ('MARY', 'MARY', 'IX', 'VISIT') 0 -8.870747900000012 -88.51668964752407
sentence ('MARY', 'JOHN', 'GIVE', 'IX') 0 -6.2161681999999985 -61.97087113378645
sentence ('MARY', 'JOHN', 'GIVE', 'LOVE') 0 -8.787609000000003 -87.68529787002014
sentence ('MARY', 'JOHN', 'GIVE', 'VISIT') 0 -9.238006800000008 -92.18927692987553
sentence ('MARY', 'JOHN', 'VISIT', 'IX') 0 -7.009448100000014 -69.90367089631069
sentence ('MARY', 'JOHN', 'VISIT', 'LOVE') 0 -7.813785200000012 -77.94706063254431
sentence ('MARY', 'JOHN', 'VISIT', 'VISIT') 0 -8.264183000000017 -82.4510396923997
sentence ('MARY', 'JOHN', 'IX', 'IX') 0 -6.229547289999999 -62.10466652984533
sentence ('MARY', 'JOHN', 'IX', 'LOVE') 0 -7.708158890000007 -76.89080126607905
sentence ('MARY', 'JOHN', 'IX', 'VISIT') 0 -8.158556689999998 -81.3947803259343
sentence ('MARY', 'IX', 'GIVE', 'IX') 0 -7.004880200000002 -69.85800177284237
sentence ('MARY', 'IX', 'GIVE', 'LOVE') 0 -8.22610920000001 -82.0703105090761
sentence ('MARY', 'IX', 'GIVE', 'VISIT') 0 -8.676507000000015 -86.57428956893149
sentence ('MARY', 'IX', 'VISIT', 'IX') 0 -7.905006600000007 -78.8592665353665
sentence ('MARY', 'IX', 'VISIT', 'LOVE') 0 -8.709343700000005 -86.90265627160011
sentence ('MARY', 'IX', 'VISIT', 'VISIT') 0 -9.15974150000001 -91.40663533145552
sentence ('MARY', 'IX', 'IX', 'IX') 0 -5.434840300000005 -54.15760726890127
sentence ('MARY', 'IX', 'IX', 'LOVE') 0 -8.595867800000008 -85.76790100513493
sentence ('MARY', 'IX', 'IX', 'VISIT') 0 -9.046265600000012 -90.27188006499034
sentence ('JOHN', 'MARY', 'GIVE', 'IX') 0 -5.723686700000002 -57.04605869208382
sentence ('JOHN', 'MARY', 'GIVE', 'LOVE') 0 -6.94491570000001 -69.25836742831753
sentence ('JOHN', 'MARY', 'GIVE', 'VISIT') 0 -7.3953135000000145 -73.76234648817294
sentence ('JOHN', 'MARY', 'VISIT', 'IX') 0 -5.749233600000011 -57.30152845460798
sentence ('JOHN', 'MARY', 'VISIT', 'LOVE') 0 -6.553570700000009 -65.3449181908416
sentence ('JOHN', 'MARY', 'VISIT', 'VISIT') 0 -7.003968500000013 -69.848897250697
sentence ('JOHN', 'MARY', 'IX', 'IX') 0 -5.130270600000017 -51.11190218814284
sentence ('JOHN', 'MARY', 'IX', 'LOVE') 0 -6.608882200000011 -65.89803692437641
sentence ('JOHN', 'MARY', 'IX', 'VISIT') 0 -7.059280000000015 -70.40201598423181
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sentence ('JOHN', 'JOHN', 'GIVE', 'LOVE') 0 -7.919561799999997 -79.00483120672777
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sentence ('JOHN', 'JOHN', 'VISIT', 'VISIT') 0 -7.39613580000001 -73.77057302910734
sentence ('JOHN', 'JOHN', 'IX', 'IX') 0 -5.361500089999993 -53.424199866552975
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sentence ('JOHN', 'JOHN', 'IX', 'VISIT') 0 -7.290509489999991 -72.71431366264194
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sentence ('JOHN', 'IX', 'GIVE', 'LOVE') 0 -6.157114890000003 -61.38037274578372
sentence ('JOHN', 'IX', 'GIVE', 'VISIT') 0 -6.607512689999993 -65.88435180563897
sentence ('JOHN', 'IX', 'VISIT', 'IX') 0 -5.836012289999999 -58.16932877207413
sentence ('JOHN', 'IX', 'VISIT', 'LOVE') 0 -6.640349389999997 -66.21271850830774
sentence ('JOHN', 'IX', 'VISIT', 'VISIT') 0 -7.090747190000002 -70.71669756816314
sentence ('JOHN', 'IX', 'IX', 'IX') 0 -3.365845989999997 -33.467669505608896
sentence ('JOHN', 'IX', 'IX', 'LOVE') 0 -6.52687349 -65.07796324184257
sentence ('JOHN', 'IX', 'IX', 'VISIT') 0 -6.9772712900000045 -69.58194230169796
Best sentence (-33.467669505608896, ('JOHN', 'IX', 'IX', 'IX'), 0, -3.365845989999997)
num_sentences 81 0.08010161599668209 seconds
0 candidates ['JOHN', 'IX']
1 candidates ['FUTURE', 'IX', 'JOHN']
2 candidates ['MARY', 'JOHN', 'IX']
3 candidates ['BUY', 'ARRIVE', 'BOOK']
4 candidates ['HOUSE', 'CAN', 'TEACHER']
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sentence ('JOHN', 'FUTURE', 'MARY', 'BUY', 'CAN') 0 -8.539693799999995 -85.21510438355324
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sentence ('JOHN', 'FUTURE', 'MARY', 'ARRIVE', 'HOUSE') 0 -9.029955700000002 -90.11772112526592
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sentence ('JOHN', 'FUTURE', 'IX', 'BUY', 'HOUSE') 0 -5.8591041299999915 -58.40920572875991
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sentence ('JOHN', 'FUTURE', 'IX', 'BUY', 'TEACHER') 0 -9.161816099999996 -91.43633147614443
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sentence ('JOHN', 'FUTURE', 'IX', 'BOOK', 'TEACHER') 0 -8.642719299999982 -86.24536977689394
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sentence ('JOHN', 'IX', 'MARY', 'ARRIVE', 'HOUSE') 0 -8.792204190000007 -87.74021976956998
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sentence ('IX', 'IX', 'MARY', 'BUY', 'HOUSE') 0 -7.825165729999995 -78.06984565529902
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sentence ('IX', 'JOHN', 'IX', 'BUY', 'CAN') 0 -9.317201189999992 -92.99021755284349
sentence ('IX', 'JOHN', 'IX', 'BUY', 'TEACHER') 0 -10.158144789999994 -101.39965513920532
sentence ('IX', 'JOHN', 'IX', 'ARRIVE', 'HOUSE') 0 -9.807463090000013 -97.89283429455631
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sentence ('IX', 'JOHN', 'IX', 'ARRIVE', 'TEACHER') 0 -10.276200990000007 -102.58021934194073
sentence ('IX', 'JOHN', 'IX', 'BOOK', 'HOUSE') 0 -9.170310090000001 -91.52130839257055
sentence ('IX', 'JOHN', 'IX', 'BOOK', 'CAN') 0 -8.798104389999992 -87.79925585359314
sentence ('IX', 'JOHN', 'IX', 'BOOK', 'TEACHER') 0 -9.639047989999995 -96.20869343995497
Best sentence (-58.2199148668346, ('JOHN', 'IX', 'MARY', 'BUY', 'HOUSE'), 0, -5.840173919999998)
num_sentences 162 0.22867282199149486 seconds
0 candidates ['JOHN', 'SOMETHING-ONE']
1 candidates ['FUTURE', 'VISIT', 'SOMETHING-ONE']
2 candidates ['BLAME', 'GIVE', 'JOHN']
3 candidates ['MARY', 'GIVE', 'BOX']
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sentence ('JOHN', 'VISIT', 'JOHN', 'GIVE') 0 -7.759126899999998 -77.37119012332641
sentence ('JOHN', 'VISIT', 'JOHN', 'BOX') 0 -6.064344640000002 -60.42307366778287
sentence ('JOHN', 'SOMETHING-ONE', 'BLAME', 'MARY') 0 -6.374274500000013 -63.522360279243536
sentence ('JOHN', 'SOMETHING-ONE', 'BLAME', 'GIVE') 0 -8.213167000000013 -81.9115920186562
sentence ('JOHN', 'SOMETHING-ONE', 'BLAME', 'BOX') 0 -7.696277600000002 -76.74240416311251
sentence ('JOHN', 'SOMETHING-ONE', 'GIVE', 'MARY') 0 -6.878810200000004 -68.56793961559819
sentence ('JOHN', 'SOMETHING-ONE', 'GIVE', 'GIVE') 0 -6.884433200000004 -68.62447635501084
sentence ('JOHN', 'SOMETHING-ONE', 'GIVE', 'BOX') 0 -7.466306500000002 -74.44291549946725
sentence ('JOHN', 'SOMETHING-ONE', 'JOHN', 'MARY') 0 -6.485324599999998 -64.63286246125651
sentence ('JOHN', 'SOMETHING-ONE', 'JOHN', 'GIVE') 0 -8.264173499999998 -82.42165820066916
sentence ('JOHN', 'SOMETHING-ONE', 'JOHN', 'BOX') 0 -6.569391240000002 -65.47354174512563
sentence ('SOMETHING-ONE', 'FUTURE', 'BLAME', 'MARY') 0 -7.3360652000000215 -73.1402979291732
sentence ('SOMETHING-ONE', 'FUTURE', 'BLAME', 'GIVE') 0 -9.174957700000022 -91.52952966858585
sentence ('SOMETHING-ONE', 'FUTURE', 'BLAME', 'BOX') 0 -8.65806830000001 -86.36034181304217
sentence ('SOMETHING-ONE', 'FUTURE', 'GIVE', 'MARY') 0 -7.8406009000000125 -78.18587726552784
sentence ('SOMETHING-ONE', 'FUTURE', 'GIVE', 'GIVE') 0 -7.8462239000000125 -78.2424140049405
sentence ('SOMETHING-ONE', 'FUTURE', 'GIVE', 'BOX') 0 -8.42809720000001 -84.0608531493969
sentence ('SOMETHING-ONE', 'FUTURE', 'JOHN', 'MARY') 0 -6.9401821000000155 -69.18146811118625
sentence ('SOMETHING-ONE', 'FUTURE', 'JOHN', 'GIVE') 0 -8.719031000000015 -86.9702638505989
sentence ('SOMETHING-ONE', 'FUTURE', 'JOHN', 'BOX') 0 -7.024248740000019 -70.02214739505536
sentence ('SOMETHING-ONE', 'VISIT', 'BLAME', 'MARY') 0 -7.323752000000013 -73.01717830944054
sentence ('SOMETHING-ONE', 'VISIT', 'BLAME', 'GIVE') 0 -9.162644500000013 -91.40641004885319
sentence ('SOMETHING-ONE', 'VISIT', 'BLAME', 'BOX') 0 -8.645755100000002 -86.23722219330952
sentence ('SOMETHING-ONE', 'VISIT', 'GIVE', 'MARY') 0 -7.828287700000004 -78.06275764579519
sentence ('SOMETHING-ONE', 'VISIT', 'GIVE', 'GIVE') 0 -7.833910700000004 -78.11929438520784
sentence ('SOMETHING-ONE', 'VISIT', 'GIVE', 'BOX') 0 -8.415784000000002 -83.93773352966426
sentence ('SOMETHING-ONE', 'VISIT', 'JOHN', 'MARY') 0 -7.434802099999999 -74.1276804914535
sentence ('SOMETHING-ONE', 'VISIT', 'JOHN', 'GIVE') 0 -9.213650999999999 -91.91647623086615
sentence ('SOMETHING-ONE', 'VISIT', 'JOHN', 'BOX') 0 -7.518868740000002 -74.96835977532263
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'BLAME', 'MARY') 0 -7.217965900000024 -71.95931938678339
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'BLAME', 'GIVE') 0 -9.056858400000024 -90.34855112619606
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'BLAME', 'BOX') 0 -8.539969000000013 -85.17936327065237
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'GIVE', 'MARY') 0 -7.722501600000015 -77.00489872313804
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'GIVE', 'GIVE') 0 -7.728124600000015 -77.0614354625507
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'GIVE', 'BOX') 0 -8.309997900000013 -82.87987460700711
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'JOHN', 'MARY') 0 -7.32901600000001 -73.06982156879637
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'JOHN', 'GIVE') 0 -9.10786490000001 -90.85861730820902
sentence ('SOMETHING-ONE', 'SOMETHING-ONE', 'JOHN', 'BOX') 0 -7.413082640000013 -73.91050085266548
Best sentence (-49.580916003646394, ('JOHN', 'FUTURE', 'JOHN', 'MARY'), 0, -4.980131400000005)
num_sentences 54 0.05478229899017606 seconds
0 candidates ['IX', 'JOHN', 'MARY']
1 candidates ['VISIT', 'GO', 'CORN']
2 candidates ['GIVE', 'IX', 'MARY']
3 candidates ['MARY', 'IX', 'GO']
sentence ('IX', 'VISIT', 'GIVE', 'MARY') 0 -7.741266400000001 -77.22167708729938
sentence ('IX', 'VISIT', 'GIVE', 'IX') 0 -7.345806699999997 -73.26708762027677
sentence ('IX', 'VISIT', 'GIVE', 'GO') 0 -8.911647399999993 -88.92550092381099
sentence ('IX', 'VISIT', 'IX', 'MARY') 0 -7.4052329000000015 -73.86134595462036
sentence ('IX', 'VISIT', 'IX', 'IX') 0 -6.7523906000000125 -67.33293048759788
sentence ('IX', 'VISIT', 'IX', 'GO') 0 -8.575613899999993 -85.56516979113196
sentence ('IX', 'VISIT', 'MARY', 'MARY') 0 -6.701231500000006 -66.82133644022674
sentence ('IX', 'VISIT', 'MARY', 'IX') 0 -6.722663699999998 -67.03566597320408
sentence ('IX', 'VISIT', 'MARY', 'GO') 0 -7.871612499999998 -78.52516027673833
sentence ('IX', 'GO', 'GIVE', 'MARY') 0 -7.635480300000012 -76.16381967868712
sentence ('IX', 'GO', 'GIVE', 'IX') 0 -7.240020600000008 -72.2092302116645
sentence ('IX', 'GO', 'GIVE', 'GO') 0 -8.805861300000004 -87.86764351519872
sentence ('IX', 'GO', 'IX', 'MARY') 0 -7.498846900000018 -74.79748954600815
sentence ('IX', 'GO', 'IX', 'IX') 0 -6.846004600000015 -68.26907407898553
sentence ('IX', 'GO', 'IX', 'GO') 0 -8.66922790000001 -86.50131338251975
sentence ('IX', 'GO', 'MARY', 'MARY') 0 -7.518268100000014 -74.99170603161444
sentence ('IX', 'GO', 'MARY', 'IX') 0 -7.539700300000007 -75.20603556459179
sentence ('IX', 'GO', 'MARY', 'GO') 0 -8.688649100000006 -86.69552986812604
sentence ('IX', 'CORN', 'GIVE', 'MARY') 0 -8.277171300000006 -82.58073057433656
sentence ('IX', 'CORN', 'GIVE', 'IX') 0 -7.881711600000003 -78.62614110731394
sentence ('IX', 'CORN', 'GIVE', 'GO') 0 -9.447552299999998 -94.28455441084816
sentence ('IX', 'CORN', 'IX', 'MARY') 0 -7.941137799999993 -79.22039944165739
sentence ('IX', 'CORN', 'IX', 'IX') 0 -7.288295500000004 -72.69198397463492
sentence ('IX', 'CORN', 'IX', 'GO') 0 -9.111518799999985 -90.924223278169
sentence ('IX', 'CORN', 'MARY', 'MARY') 0 -8.159959100000009 -81.40861692726389
sentence ('IX', 'CORN', 'MARY', 'IX') 0 -8.181391300000001 -81.62294646024122
sentence ('IX', 'CORN', 'MARY', 'GO') 0 -9.3303401 -93.11244076377548
sentence ('JOHN', 'VISIT', 'GIVE', 'MARY') 0 -6.373763600000004 -63.54665787761458
sentence ('JOHN', 'VISIT', 'GIVE', 'IX') 0 -5.9783039 -59.592068410591956
sentence ('JOHN', 'VISIT', 'GIVE', 'GO') 0 -7.544144599999996 -75.25048171412618
sentence ('JOHN', 'VISIT', 'IX', 'MARY') 0 -6.0377301000000045 -60.186326744935556
sentence ('JOHN', 'VISIT', 'IX', 'IX') 0 -5.3848878000000155 -53.65791127791308
sentence ('JOHN', 'VISIT', 'IX', 'GO') 0 -7.208111099999996 -71.89015058144714
sentence ('JOHN', 'VISIT', 'MARY', 'MARY') 0 -5.333728700000009 -53.14631723054193
sentence ('JOHN', 'VISIT', 'MARY', 'IX') 0 -5.355160900000001 -53.36064676351927
sentence ('JOHN', 'VISIT', 'MARY', 'GO') 0 -6.504109700000001 -64.85014106705353
sentence ('JOHN', 'GO', 'GIVE', 'MARY') 0 -6.095778600000003 -60.76681146900219
sentence ('JOHN', 'GO', 'GIVE', 'IX') 0 -5.700318899999999 -56.81222200197957
sentence ('JOHN', 'GO', 'GIVE', 'GO') 0 -7.266159599999995 -72.4706353055138
sentence ('JOHN', 'GO', 'IX', 'MARY') 0 -5.959145200000009 -59.40048133632322
sentence ('JOHN', 'GO', 'IX', 'IX') 0 -5.306302900000006 -52.872065869300606
sentence ('JOHN', 'GO', 'IX', 'GO') 0 -7.129526200000001 -71.10430517283481
sentence ('JOHN', 'GO', 'MARY', 'MARY') 0 -5.97856640000002 -59.594697821929664
sentence ('JOHN', 'GO', 'MARY', 'IX') 0 -5.999998600000012 -59.809027354907
sentence ('JOHN', 'GO', 'MARY', 'GO') 0 -7.1489474000000115 -71.29852165844126
sentence ('JOHN', 'CORN', 'GIVE', 'MARY') 0 -7.520501200000012 -75.01403836465178
sentence ('JOHN', 'CORN', 'GIVE', 'IX') 0 -7.125041500000009 -71.05944889762917
sentence ('JOHN', 'CORN', 'GIVE', 'GO') 0 -8.690882200000004 -86.71786220116338
sentence ('JOHN', 'CORN', 'IX', 'MARY') 0 -7.184467699999999 -71.65370723197262
sentence ('JOHN', 'CORN', 'IX', 'IX') 0 -6.53162540000001 -65.12529176495015
sentence ('JOHN', 'CORN', 'IX', 'GO') 0 -8.354848699999991 -83.35753106848422
sentence ('JOHN', 'CORN', 'MARY', 'MARY') 0 -7.403289000000015 -73.84192471757912
sentence ('JOHN', 'CORN', 'MARY', 'IX') 0 -7.424721200000008 -74.05625425055645
sentence ('JOHN', 'CORN', 'MARY', 'GO') 0 -8.573670000000007 -85.54574855409071
sentence ('MARY', 'VISIT', 'GIVE', 'MARY') 0 -7.656481500000012 -76.37384816326075
sentence ('MARY', 'VISIT', 'GIVE', 'IX') 0 -7.261021800000009 -72.41925869623812
sentence ('MARY', 'VISIT', 'GIVE', 'GO') 0 -8.826862500000004 -88.07767199977233
sentence ('MARY', 'VISIT', 'IX', 'MARY') 0 -7.320448000000013 -73.01351703058171
sentence ('MARY', 'VISIT', 'IX', 'IX') 0 -6.667605700000024 -66.48510156355924
sentence ('MARY', 'VISIT', 'IX', 'GO') 0 -8.490829000000005 -84.71734086709331
sentence ('MARY', 'VISIT', 'MARY', 'MARY') 0 -6.6164466000000175 -65.97350751618809
sentence ('MARY', 'VISIT', 'MARY', 'IX') 0 -6.63787880000001 -66.18783704916542
sentence ('MARY', 'VISIT', 'MARY', 'GO') 0 -7.786827600000009 -77.6773313526997
sentence ('MARY', 'GO', 'GIVE', 'MARY') 0 -8.11382180000001 -80.94725475464836
sentence ('MARY', 'GO', 'GIVE', 'IX') 0 -7.718362100000007 -76.99266528762573
sentence ('MARY', 'GO', 'GIVE', 'GO') 0 -9.284202800000003 -92.65107859115994
sentence ('MARY', 'GO', 'IX', 'MARY') 0 -7.977188400000017 -79.58092462196937
sentence ('MARY', 'GO', 'IX', 'IX') 0 -7.324346100000014 -73.05250915494676
sentence ('MARY', 'GO', 'IX', 'GO') 0 -9.147569400000009 -91.28474845848098
sentence ('MARY', 'GO', 'MARY', 'MARY') 0 -7.9966096000000135 -79.77514110757568
sentence ('MARY', 'GO', 'MARY', 'IX') 0 -8.018041800000006 -79.98947064055301
sentence ('MARY', 'GO', 'MARY', 'GO') 0 -9.166990600000005 -91.47896494408727
sentence ('MARY', 'CORN', 'GIVE', 'MARY') 0 -8.75551280000002 -87.36416565029793
sentence ('MARY', 'CORN', 'GIVE', 'IX') 0 -8.360053100000016 -83.4095761832753
sentence ('MARY', 'CORN', 'GIVE', 'GO') 0 -9.925893800000011 -99.06798948680952
sentence ('MARY', 'CORN', 'IX', 'MARY') 0 -8.419479300000006 -84.00383451761877
sentence ('MARY', 'CORN', 'IX', 'IX') 0 -7.766637000000017 -77.47541905059629
sentence ('MARY', 'CORN', 'IX', 'GO') 0 -9.589860299999998 -95.70765835413036
sentence ('MARY', 'CORN', 'MARY', 'MARY') 0 -8.638300600000022 -86.19205200322526
sentence ('MARY', 'CORN', 'MARY', 'IX') 0 -8.659732800000015 -86.4063815362026
sentence ('MARY', 'CORN', 'MARY', 'GO') 0 -9.808681600000014 -97.89587583973686
Best sentence (-52.872065869300606, ('JOHN', 'GO', 'IX', 'IX'), 0, -5.306302900000006)
num_sentences 81 0.08105988500756212 seconds
0 candidates ['JOHN', 'IX', 'MARY']
1 candidates ['BLAME', 'MARY', 'ARRIVE']
2 candidates ['MARY', 'LOVE', 'BLAME']
sentence ('JOHN', 'BLAME', 'MARY') 0 -3.9967439000000127 -39.77981312862073
sentence ('JOHN', 'BLAME', 'LOVE') 0 -5.5570200000000085 -55.38259178677643
sentence ('JOHN', 'BLAME', 'BLAME') 0 -5.269226400000008 -52.504659315333754
sentence ('JOHN', 'MARY', 'MARY') 0 -4.137870800000016 -41.19110024992322
sentence ('JOHN', 'MARY', 'LOVE') 0 -3.291706700000006 -32.72947690807886
sentence ('JOHN', 'MARY', 'BLAME') 0 -3.9779781000000014 -39.59219443663614
sentence ('JOHN', 'ARRIVE', 'MARY') 0 -4.347690200000002 -43.289295456750075
sentence ('JOHN', 'ARRIVE', 'LOVE') 0 -5.173459500000007 -51.547006114905855
sentence ('JOHN', 'ARRIVE', 'BLAME') 0 -4.8856659000000064 -48.66907364346318
sentence ('IX', 'BLAME', 'MARY') 0 -5.083673700000006 -50.64912461337983
sentence ('IX', 'BLAME', 'LOVE') 0 -6.6439498000000015 -66.25190327153553
sentence ('IX', 'BLAME', 'BLAME') 0 -6.356156200000001 -63.373970800092856
sentence ('IX', 'MARY', 'MARY') 0 -5.4709972000000135 -54.522377734682365
sentence ('IX', 'MARY', 'LOVE') 0 -4.624833100000004 -46.06075439283801
sentence ('IX', 'MARY', 'BLAME') 0 -5.311104499999999 -52.92347192139529
sentence ('IX', 'ARRIVE', 'MARY') 0 -5.831247399999995 -58.12488094150917
sentence ('IX', 'ARRIVE', 'LOVE') 0 -6.6570167 -66.38259159966496
sentence ('IX', 'ARRIVE', 'BLAME') 0 -6.369223099999999 -63.50465912822228
sentence ('MARY', 'BLAME', 'MARY') 0 -4.8641468000000145 -48.45385596266889
sentence ('MARY', 'BLAME', 'LOVE') 0 -6.42442290000001 -64.05663462082458
sentence ('MARY', 'BLAME', 'BLAME') 0 -6.13662930000001 -61.178702149381905
sentence ('MARY', 'MARY', 'MARY') 0 -5.949338700000013 -59.305793083971324
sentence ('MARY', 'MARY', 'LOVE') 0 -5.103174600000003 -50.844169742126965
sentence ('MARY', 'MARY', 'BLAME') 0 -5.789445999999998 -57.70688727068424
sentence ('MARY', 'ARRIVE', 'MARY') 0 -6.3095889000000085 -62.90829629079827
sentence ('MARY', 'ARRIVE', 'LOVE') 0 -7.135358200000013 -71.16600694895405
sentence ('MARY', 'ARRIVE', 'BLAME') 0 -6.8475646000000125 -68.28807447751137
Best sentence (-32.729476908078858, ('JOHN', 'MARY', 'LOVE'), 0, -3.291706700000006)
num_sentences 27 0.02183191999210976 seconds
0 candidates ['MARY', 'JOHN', 'HOMEWORK']
1 candidates ['BUY', 'ARRIVE', 'NEW']
2 candidates ['VISIT', 'NEW', 'CAR']
3 candidates ['BOOK', 'BROTHER', 'NEW']
sentence ('MARY', 'BUY', 'VISIT', 'BOOK') 0 -8.29800520000002 -82.78593120270621
sentence ('MARY', 'BUY', 'VISIT', 'BROTHER') 0 -10.156661700000015 -101.37249878044194
sentence ('MARY', 'BUY', 'VISIT', 'NEW') 0 -9.855037700000011 -98.35625889590978
sentence ('MARY', 'BUY', 'NEW', 'BOOK') 0 -8.532286100000022 -85.12874360690337
sentence ('MARY', 'BUY', 'NEW', 'BROTHER') 0 -10.390942600000002 -103.71531118463895
sentence ('MARY', 'BUY', 'NEW', 'NEW') 0 -10.089318600000013 -100.69907130010694
sentence ('MARY', 'BUY', 'CAR', 'BOOK') 0 -7.266127300000008 -72.46715773019521
sentence ('MARY', 'BUY', 'CAR', 'BROTHER') 0 -9.124783800000003 -91.05372530793095
sentence ('MARY', 'BUY', 'CAR', 'NEW') 0 -8.823159799999999 -88.03748542339878
sentence ('MARY', 'ARRIVE', 'VISIT', 'BOOK') 0 -8.416061400000018 -83.96649327121621
sentence ('MARY', 'ARRIVE', 'VISIT', 'BROTHER') 0 -10.274717900000013 -102.55306084895194
sentence ('MARY', 'ARRIVE', 'VISIT', 'NEW') 0 -9.97309390000001 -99.53682096441979
sentence ('MARY', 'ARRIVE', 'NEW', 'BOOK') 0 -8.65034230000002 -86.30930567541337
sentence ('MARY', 'ARRIVE', 'NEW', 'BROTHER') 0 -10.5089988 -104.89587325314896
sentence ('MARY', 'ARRIVE', 'NEW', 'NEW') 0 -10.20737480000001 -101.87963336861694
sentence ('MARY', 'ARRIVE', 'CAR', 'BOOK') 0 -8.394872100000015 -83.7546057987053
sentence ('MARY', 'ARRIVE', 'CAR', 'BROTHER') 0 -10.25352860000001 -102.34117337644103
sentence ('MARY', 'ARRIVE', 'CAR', 'NEW') 0 -9.951904600000006 -99.32493349190888
sentence ('MARY', 'NEW', 'VISIT', 'BOOK') 0 -8.579085700000007 -85.59673793431097
sentence ('MARY', 'NEW', 'VISIT', 'BROTHER') 0 -10.437742200000002 -104.1833055120467
sentence ('MARY', 'NEW', 'VISIT', 'NEW') 0 -10.136118199999999 -101.16706562751453
sentence ('MARY', 'NEW', 'NEW', 'BOOK') 0 -8.813366600000023 -87.93955033850827
sentence ('MARY', 'NEW', 'NEW', 'BROTHER') 0 -10.672023100000004 -106.52611791624386
sentence ('MARY', 'NEW', 'NEW', 'NEW') 0 -10.370399100000014 -103.50987803171184
sentence ('MARY', 'NEW', 'CAR', 'BOOK') 0 -7.54855910000002 -75.29147746180021
sentence ('MARY', 'NEW', 'CAR', 'BROTHER') 0 -9.4072156 -93.8780450395358
sentence ('MARY', 'NEW', 'CAR', 'NEW') 0 -9.105591600000011 -90.86180515500378
sentence ('JOHN', 'BUY', 'VISIT', 'BOOK') 0 -6.316779600000004 -62.973676586781714
sentence ('JOHN', 'BUY', 'VISIT', 'BROTHER') 0 -8.175436099999999 -81.56024416451744
sentence ('JOHN', 'BUY', 'VISIT', 'NEW') 0 -7.873812099999995 -78.54400427998529
sentence ('JOHN', 'BUY', 'NEW', 'BOOK') 0 -6.5510605000000055 -65.31648899097887
sentence ('JOHN', 'BUY', 'NEW', 'BROTHER') 0 -8.409716999999986 -83.90305656871446
sentence ('JOHN', 'BUY', 'NEW', 'NEW') 0 -8.108092999999997 -80.88681668418245
sentence ('JOHN', 'BUY', 'CAR', 'BOOK') 0 -4.050728399999997 -40.31317011427077
sentence ('JOHN', 'BUY', 'CAR', 'BROTHER') 0 -5.909384899999992 -58.899737692006504
sentence ('JOHN', 'BUY', 'CAR', 'NEW') 0 -5.607760899999988 -55.88349780747434
sentence ('JOHN', 'ARRIVE', 'VISIT', 'BOOK') 0 -6.454162700000012 -64.34750765529182
sentence ('JOHN', 'ARRIVE', 'VISIT', 'BROTHER') 0 -8.312819200000007 -82.93407523302756
sentence ('JOHN', 'ARRIVE', 'VISIT', 'NEW') 0 -8.011195200000003 -79.91783534849539
sentence ('JOHN', 'ARRIVE', 'NEW', 'BOOK') 0 -6.688443600000014 -66.69032005948898
sentence ('JOHN', 'ARRIVE', 'NEW', 'BROTHER') 0 -8.547100099999994 -85.27688763722458
sentence ('JOHN', 'ARRIVE', 'NEW', 'NEW') 0 -8.245476100000005 -82.26064775269255
sentence ('JOHN', 'ARRIVE', 'CAR', 'BOOK') 0 -6.432973400000009 -64.13562018278091
sentence ('JOHN', 'ARRIVE', 'CAR', 'BROTHER') 0 -8.291629900000004 -82.72218776051665
sentence ('JOHN', 'ARRIVE', 'CAR', 'NEW') 0 -7.9900059 -79.70594787598448
sentence ('JOHN', 'NEW', 'VISIT', 'BOOK') 0 -7.3440741 -73.24662331838657
sentence ('JOHN', 'NEW', 'VISIT', 'BROTHER') 0 -9.202730599999995 -91.8331908961223
sentence ('JOHN', 'NEW', 'VISIT', 'NEW') 0 -8.901106599999991 -88.81695101159013
sentence ('JOHN', 'NEW', 'NEW', 'BOOK') 0 -7.578355000000016 -75.58943572258387
sentence ('JOHN', 'NEW', 'NEW', 'BROTHER') 0 -9.437011499999997 -94.17600330031946
sentence ('JOHN', 'NEW', 'NEW', 'NEW') 0 -9.135387500000007 -91.15976341578744
sentence ('JOHN', 'NEW', 'CAR', 'BOOK') 0 -6.313547500000013 -62.941362845875815
sentence ('JOHN', 'NEW', 'CAR', 'BROTHER') 0 -8.172203999999994 -81.52793042361141
sentence ('JOHN', 'NEW', 'CAR', 'NEW') 0 -7.870580000000004 -78.51169053907938
sentence ('HOMEWORK', 'BUY', 'VISIT', 'BOOK') 0 -9.63028150000001 -96.10869945905175
sentence ('HOMEWORK', 'BUY', 'VISIT', 'BROTHER') 0 -11.488938000000005 -114.69526703678748
sentence ('HOMEWORK', 'BUY', 'VISIT', 'NEW') 0 -11.187314 -111.67902715225532
sentence ('HOMEWORK', 'BUY', 'NEW', 'BOOK') 0 -9.864562400000011 -98.45151186324891
sentence ('HOMEWORK', 'BUY', 'NEW', 'BROTHER') 0 -11.723218899999992 -117.0380794409845
sentence ('HOMEWORK', 'BUY', 'NEW', 'NEW') 0 -11.421594900000002 -114.02183955645248
sentence ('HOMEWORK', 'BUY', 'CAR', 'BOOK') 0 -8.598403599999997 -85.78992598654075
sentence ('HOMEWORK', 'BUY', 'CAR', 'BROTHER') 0 -10.457060099999993 -104.37649356427649
sentence ('HOMEWORK', 'BUY', 'CAR', 'NEW') 0 -10.155436099999989 -101.36025367974432
sentence ('HOMEWORK', 'ARRIVE', 'VISIT', 'BOOK') 0 -9.748337700000008 -97.28926152756176
sentence ('HOMEWORK', 'ARRIVE', 'VISIT', 'BROTHER') 0 -11.606994200000003 -115.87582910529748
sentence ('HOMEWORK', 'ARRIVE', 'VISIT', 'NEW') 0 -11.305370199999999 -112.85958922076533
sentence ('HOMEWORK', 'ARRIVE', 'NEW', 'BOOK') 0 -9.98261860000001 -99.63207393175891
sentence ('HOMEWORK', 'ARRIVE', 'NEW', 'BROTHER') 0 -11.84127509999999 -118.2186415094945
sentence ('HOMEWORK', 'ARRIVE', 'NEW', 'NEW') 0 -11.5396511 -115.20240162496249
sentence ('HOMEWORK', 'ARRIVE', 'CAR', 'BOOK') 0 -9.727148400000004 -97.07737405505085
sentence ('HOMEWORK', 'ARRIVE', 'CAR', 'BROTHER') 0 -11.5858049 -115.66394163278659
sentence ('HOMEWORK', 'ARRIVE', 'CAR', 'NEW') 0 -11.284180899999996 -112.64770174825442
sentence ('HOMEWORK', 'NEW', 'VISIT', 'BOOK') 0 -9.911361999999997 -98.9195061906565
sentence ('HOMEWORK', 'NEW', 'VISIT', 'BROTHER') 0 -11.770018499999992 -117.50607376839224
sentence ('HOMEWORK', 'NEW', 'VISIT', 'NEW') 0 -11.468394499999988 -114.48983388386009
sentence ('HOMEWORK', 'NEW', 'NEW', 'BOOK') 0 -10.145642900000013 -101.26231859485381
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sentence ('HOMEWORK', 'NEW', 'NEW', 'NEW') 0 -11.702675400000004 -116.83264628805738
sentence ('HOMEWORK', 'NEW', 'CAR', 'BOOK') 0 -8.88083540000001 -88.61424571814575
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sentence ('HOMEWORK', 'NEW', 'CAR', 'NEW') 0 -10.4378679 -104.18457341134933
Best sentence (-40.313170114270768, ('JOHN', 'BUY', 'CAR', 'BOOK'), 0, -4.050728399999997)
num_sentences 81 0.07957180500670802 seconds
0 candidates ['MARY', 'MAN', 'GIVE']
1 candidates ['IX', 'SHOULD', 'JOHN']
2 candidates ['WOMAN', 'IX', 'JOHN']
3 candidates ['IX', 'WOMAN', 'JOHN']
4 candidates ['IX', 'WOMAN', 'JOHN']
5 candidates ['NEW', 'ARRIVE', 'BUY']
6 candidates ['COAT', 'BREAK-DOWN', 'BOOK']
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sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'IX', 'BUY', 'COAT') 0 -14.826855089999995 -148.03437838291282
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'IX', 'BUY', 'BREAK-DOWN') 0 -14.826855089999995 -148.03437879557057
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'IX', 'BUY', 'BOOK') 0 -13.655899989999995 -136.32482945879175
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'NEW', 'COAT') 0 -13.015356389999994 -129.9194106001239
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'NEW', 'BREAK-DOWN') 0 -15.836826799999997 -158.1341151127817
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'NEW', 'BOOK') 0 -14.665871699999997 -146.42456577600288
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'ARRIVE', 'COAT') 0 -14.438910499999992 -144.15495223236005
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'ARRIVE', 'BREAK-DOWN') 0 -14.438910499999992 -144.1549526450178
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'ARRIVE', 'BOOK') 0 -13.267955399999991 -132.44540330823898
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'BUY', 'COAT') 0 -15.555746299999996 -155.3233107336392
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'BUY', 'BREAK-DOWN') 0 -15.555746299999996 -155.32331114629696
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'WOMAN', 'BUY', 'BOOK') 0 -14.384791199999995 -143.61376180951814
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'NEW', 'COAT') 0 -12.577347889999999 -125.53938074862671
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'NEW', 'BREAK-DOWN') 0 -15.398818300000002 -153.7540852612845
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'NEW', 'BOOK') 0 -14.227863200000002 -142.04453592450568
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'ARRIVE', 'COAT') 0 -15.367248500000002 -153.4383873808629
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'ARRIVE', 'BREAK-DOWN') 0 -15.367248500000002 -153.43838779352063
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'ARRIVE', 'BOOK') 0 -14.196293400000002 -141.72883845674184
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'BUY', 'COAT') 0 -15.630551400000016 -156.07141688214216
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'BUY', 'BREAK-DOWN') 0 -15.630551400000016 -156.0714172947999
sentence ('GIVE', 'JOHN', 'JOHN', 'JOHN', 'JOHN', 'BUY', 'BOOK') 0 -14.459596300000015 -144.3618679580211
Best sentence (-86.60049507817665, ('MARY', 'IX', 'IX', 'IX', 'IX', 'NEW', 'COAT'), 0, -8.683501890000002)
num_sentences 2187 3.727280809005606 seconds
0 candidates ['JOHN']
1 candidates ['IX', 'WOMAN', 'GIVE']
2 candidates ['IX']
3 candidates ['IX']
4 candidates ['WOMAN', 'IX', 'VISIT']
5 candidates ['BOOK', 'VIDEOTAPE', 'CHOCOLATE']
sentence ('JOHN', 'IX', 'IX', 'IX', 'WOMAN', 'BOOK') 0 -6.410954759999996 -63.90610732748821
sentence ('JOHN', 'IX', 'IX', 'IX', 'WOMAN', 'VIDEOTAPE') 0 -7.83266879 -78.12324843061624
sentence ('JOHN', 'IX', 'IX', 'IX', 'WOMAN', 'CHOCOLATE') 0 -8.589695589999991 -85.69351660553527
sentence ('JOHN', 'IX', 'IX', 'IX', 'IX', 'BOOK') 0 -5.648150989999991 -56.27808548856973
sentence ('JOHN', 'IX', 'IX', 'IX', 'IX', 'VIDEOTAPE') 0 -7.002930890000002 -69.82588529169784
sentence ('JOHN', 'IX', 'IX', 'IX', 'IX', 'CHOCOLATE') 0 -7.759957689999993 -77.39615346661687
sentence ('JOHN', 'IX', 'IX', 'IX', 'VISIT', 'BOOK') 0 -8.164741890000002 -81.44399541430532
sentence ('JOHN', 'IX', 'IX', 'IX', 'VISIT', 'VIDEOTAPE') 0 -8.798822690000009 -87.78480421743339
sentence ('JOHN', 'IX', 'IX', 'IX', 'VISIT', 'CHOCOLATE') 0 -9.55584949 -95.35507239235241
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'WOMAN', 'BOOK') 0 -8.610740270000008 -85.90396653562041
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'WOMAN', 'VIDEOTAPE') 0 -10.032454300000012 -100.12110763874846
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'WOMAN', 'CHOCOLATE') 0 -10.789481100000003 -107.69137581366748
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'IX', 'BOOK') 0 -7.847936500000003 -78.27594469670194
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'IX', 'VIDEOTAPE') 0 -9.202716400000014 -91.82374449983006
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'IX', 'CHOCOLATE') 0 -9.959743200000005 -99.39401267474908
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'VISIT', 'BOOK') 0 -10.364527400000014 -103.44185462243752
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'VISIT', 'VIDEOTAPE') 0 -10.998608200000021 -109.78266342556559
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'VISIT', 'CHOCOLATE') 0 -11.755635000000012 -117.35293160048461
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'WOMAN', 'BOOK') 0 -6.593972070000007 -65.73629415602517
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'WOMAN', 'VIDEOTAPE') 0 -8.01568610000001 -79.9534352591532
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'WOMAN', 'CHOCOLATE') 0 -8.772712900000002 -87.52370343407222
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'IX', 'BOOK') 0 -5.831168300000002 -58.10827231710669
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'IX', 'VIDEOTAPE') 0 -7.185948200000013 -71.6560721202348
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'IX', 'CHOCOLATE') 0 -7.942975000000004 -79.22634029515382
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'VISIT', 'BOOK') 0 -8.347759200000013 -83.27418224284227
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'VISIT', 'VIDEOTAPE') 0 -8.98184000000002 -89.61499104597034
sentence ('JOHN', 'GIVE', 'IX', 'IX', 'VISIT', 'CHOCOLATE') 0 -9.738866800000011 -97.18525922088936
Best sentence (-56.278085488569729, ('JOHN', 'IX', 'IX', 'IX', 'IX', 'BOOK'), 0, -5.648150989999991)
num_sentences 27 0.04066960599448066 seconds
0 candidates ['JOHN']
1 candidates ['WOMAN', 'IX', 'SHOULD']
2 candidates ['IX', 'WOMAN']
3 candidates ['IX', 'WOMAN']
4 candidates ['WOMAN', 'IX', 'MARY']
5 candidates ['BOOK', 'GIVE', 'NEW']
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'WOMAN', 'BOOK') 0 -8.610740270000008 -85.87765929544652
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'WOMAN', 'GIVE') 0 -10.549343700000009 -105.26382018433964
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'WOMAN', 'NEW') 0 -10.955405999999996 -109.32432049306011
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'IX', 'BOOK') 0 -7.847936500000003 -78.24964783939608
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'IX', 'GIVE') 0 -9.408152700000016 -93.85193642828932
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'IX', 'NEW') 0 -9.634502999999995 -96.1153167370097
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'MARY', 'BOOK') 0 -10.075533800000002 -100.52562654292092
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'MARY', 'GIVE') 0 -11.226504000000006 -112.03545513181406
sentence ('JOHN', 'WOMAN', 'IX', 'IX', 'MARY', 'NEW') 0 -11.632566299999993 -116.09595544053454
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'WOMAN', 'BOOK') 0 -8.736648369999997 -87.13675462376753
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'WOMAN', 'GIVE') 0 -10.675251799999998 -106.52291551266065
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'WOMAN', 'NEW') 0 -11.081314099999986 -110.58341582138112
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'IX', 'BOOK') 0 -7.840814199999997 -78.17843916771713
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'IX', 'GIVE') 0 -9.40103040000001 -93.78072775661037
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'IX', 'NEW') 0 -9.62738069999999 -96.04410806533076
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'MARY', 'BOOK') 0 -9.222855800000005 -91.99886087124206
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'MARY', 'GIVE') 0 -10.373826000000008 -103.5086894601352
sentence ('JOHN', 'WOMAN', 'IX', 'WOMAN', 'MARY', 'NEW') 0 -10.779888299999996 -107.56918976885568
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'WOMAN', 'BOOK') 0 -8.736648369999997 -87.13676385654335
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'WOMAN', 'GIVE') 0 -10.675251799999998 -106.52292474543647
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'WOMAN', 'NEW') 0 -11.081314099999986 -110.58342505415695
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'IX', 'BOOK') 0 -9.656260500000002 -96.332911400493
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'IX', 'GIVE') 0 -11.216476700000015 -111.93519998938625
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'IX', 'NEW') 0 -11.442826999999994 -114.19858029810663
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'MARY', 'BOOK') 0 -10.201441899999992 -101.78473110401775
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'MARY', 'GIVE') 0 -11.352412099999995 -113.2945596929109
sentence ('JOHN', 'WOMAN', 'WOMAN', 'IX', 'MARY', 'NEW') 0 -11.758474399999983 -117.35506000163137
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'WOMAN', 'BOOK') 0 -10.678002769999992 -106.55032218486441
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'WOMAN', 'GIVE') 0 -12.616606199999993 -125.93648307375753
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'WOMAN', 'NEW') 0 -13.02266849999998 -129.99698338247802
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'IX', 'BOOK') 0 -9.782168599999991 -97.59200672881401
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'IX', 'GIVE') 0 -11.342384800000005 -113.19429531770726
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'IX', 'NEW') 0 -11.568735099999984 -115.45767562642764
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'MARY', 'BOOK') 0 -11.1642102 -111.41242843233894
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'MARY', 'GIVE') 0 -12.315180400000003 -122.92225702123208
sentence ('JOHN', 'WOMAN', 'WOMAN', 'WOMAN', 'MARY', 'NEW') 0 -12.72124269999999 -126.98275732995256
sentence ('JOHN', 'IX', 'IX', 'IX', 'WOMAN', 'BOOK') 0 -6.410954759999996 -63.87981618852475
sentence ('JOHN', 'IX', 'IX', 'IX', 'WOMAN', 'GIVE') 0 -8.349558189999996 -83.26597707741787
sentence ('JOHN', 'IX', 'IX', 'IX', 'WOMAN', 'NEW') 0 -8.755620489999984 -87.32647738613835
sentence ('JOHN', 'IX', 'IX', 'IX', 'IX', 'BOOK') 0 -5.648150989999991 -56.251804732474305
sentence ('JOHN', 'IX', 'IX', 'IX', 'IX', 'GIVE') 0 -7.208367190000004 -71.85409332136756
sentence ('JOHN', 'IX', 'IX', 'IX', 'IX', 'NEW') 0 -7.434717489999983 -74.11747363008794
sentence ('JOHN', 'IX', 'IX', 'IX', 'MARY', 'BOOK') 0 -7.87574828999999 -78.52778343599915
sentence ('JOHN', 'IX', 'IX', 'IX', 'MARY', 'GIVE') 0 -9.026718489999993 -90.0376120248923
sentence ('JOHN', 'IX', 'IX', 'IX', 'MARY', 'NEW') 0 -9.432780789999981 -94.09811233361278
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'WOMAN', 'BOOK') 0 -8.219278759999995 -81.96307051684585
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'WOMAN', 'GIVE') 0 -10.157882189999995 -101.34923140573898
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'WOMAN', 'NEW') 0 -10.563944489999983 -105.40973171445945
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'IX', 'BOOK') 0 -7.323444589999994 -73.00475506079546
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'IX', 'GIVE') 0 -8.883660790000008 -88.6070436496887
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'IX', 'NEW') 0 -9.110011089999986 -90.87042395840909
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'MARY', 'BOOK') 0 -8.705486190000002 -86.82517676432039
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'MARY', 'GIVE') 0 -9.856456390000005 -98.33500535321353
sentence ('JOHN', 'IX', 'IX', 'WOMAN', 'MARY', 'NEW') 0 -10.262518689999993 -102.39550566193401
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'WOMAN', 'BOOK') 0 -6.403832460000004 -63.80861674962177
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'WOMAN', 'GIVE') 0 -8.342435890000004 -83.19477763851489
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'WOMAN', 'NEW') 0 -8.748498189999992 -87.25527794723537
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'IX', 'BOOK') 0 -7.323444590000008 -73.00476429357143
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'IX', 'GIVE') 0 -8.883660790000022 -88.60705288246467
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'IX', 'NEW') 0 -9.11001109 -90.87043319118506
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'MARY', 'BOOK') 0 -7.868625989999998 -78.45658399709617
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'MARY', 'GIVE') 0 -9.019596190000001 -89.96641258598932
sentence ('JOHN', 'IX', 'WOMAN', 'IX', 'MARY', 'NEW') 0 -9.42565848999999 -94.0269128947098
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'WOMAN', 'BOOK') 0 -8.345186859999998 -83.22217507794284
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'WOMAN', 'GIVE') 0 -10.283790289999999 -102.60833596683595
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'WOMAN', 'NEW') 0 -10.689852589999987 -106.66883627555643
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'IX', 'BOOK') 0 -7.449352689999998 -74.26385962189244
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'IX', 'GIVE') 0 -9.009568890000011 -89.86614821078568
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'IX', 'NEW') 0 -9.23591918999999 -92.12952851950607
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'MARY', 'BOOK') 0 -8.831394290000006 -88.08428132541736
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'MARY', 'GIVE') 0 -9.982364490000009 -99.59410991431051
sentence ('JOHN', 'IX', 'WOMAN', 'WOMAN', 'MARY', 'NEW') 0 -10.388426789999997 -103.65461022303099
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'WOMAN', 'BOOK') 0 -7.786485270000014 -77.63513001630997
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'WOMAN', 'GIVE') 0 -9.725088700000015 -97.02129090520309
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'WOMAN', 'NEW') 0 -10.131151000000003 -101.08179121392357
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'IX', 'BOOK') 0 -7.023681500000009 -70.00711856025953
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'IX', 'GIVE') 0 -8.583897700000023 -85.60940714915277
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'IX', 'NEW') 0 -8.810248000000001 -87.87278745787316
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'MARY', 'BOOK') 0 -9.251278800000009 -92.28309726378437
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'MARY', 'GIVE') 0 -10.402249000000012 -103.79292585267751
sentence ('JOHN', 'SHOULD', 'IX', 'IX', 'MARY', 'NEW') 0 -10.8083113 -107.85342616139799
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'WOMAN', 'BOOK') 0 -7.912393370000004 -78.89422534463098
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'WOMAN', 'GIVE') 0 -9.850996800000004 -98.2803862335241
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'WOMAN', 'NEW') 0 -10.257059099999992 -102.34088654224458
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'IX', 'BOOK') 0 -7.016559200000003 -69.93590988858058
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'IX', 'GIVE') 0 -8.576775400000017 -85.53819847747383
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'IX', 'NEW') 0 -8.803125699999995 -87.80157878619421
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'MARY', 'BOOK') 0 -8.398600800000011 -83.75633159210551
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'MARY', 'GIVE') 0 -9.549571000000014 -95.26616018099865
sentence ('JOHN', 'SHOULD', 'IX', 'WOMAN', 'MARY', 'NEW') 0 -9.955633300000002 -99.32666048971913
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'WOMAN', 'BOOK') 0 -7.469872170000002 -74.46902257740679
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'WOMAN', 'GIVE') 0 -9.408475600000003 -93.8551834662999
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'WOMAN', 'NEW') 0 -9.81453789999999 -97.91568377502038
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'IX', 'BOOK') 0 -8.389484300000007 -83.66517012135644
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'IX', 'GIVE') 0 -9.94970050000002 -99.26745871024968
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'IX', 'NEW') 0 -10.176050799999999 -101.53083901897007
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'MARY', 'BOOK') 0 -8.934665699999996 -89.11698982488119
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'MARY', 'GIVE') 0 -10.0856359 -100.62681841377433
sentence ('JOHN', 'SHOULD', 'WOMAN', 'IX', 'MARY', 'NEW') 0 -10.491698199999988 -104.68731872249481
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'WOMAN', 'BOOK') 0 -9.411226569999997 -93.88258090572785
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'WOMAN', 'GIVE') 0 -11.349829999999997 -113.26874179462096
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'WOMAN', 'NEW') 0 -11.755892299999985 -117.32924210334144
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'IX', 'BOOK') 0 -8.515392399999996 -84.92426544967745
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'IX', 'GIVE') 0 -10.07560860000001 -100.5265540385707
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'IX', 'NEW') 0 -10.301958899999988 -102.78993434729108
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'MARY', 'BOOK') 0 -9.897434000000004 -98.74468715320238
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'MARY', 'GIVE') 0 -11.048404200000007 -110.25451574209552
sentence ('JOHN', 'SHOULD', 'WOMAN', 'WOMAN', 'MARY', 'NEW') 0 -11.454466499999995 -114.315016050816
Best sentence (-56.251804732474305, ('JOHN', 'IX', 'IX', 'IX', 'IX', 'BOOK'), 0, -5.648150989999991)
num_sentences 108 0.15629980200901628 seconds
0 candidates ['POSS', 'IX']
1 candidates ['NEW', 'ARRIVE', 'BLAME']
2 candidates ['CAR', 'HOUSE', 'STUDENT']
3 candidates ['BREAK-DOWN']
sentence ('POSS', 'NEW', 'CAR', 'BREAK-DOWN') 0 -3.122060579999996 -31.022306352086883
sentence ('POSS', 'NEW', 'HOUSE', 'BREAK-DOWN') 0 -9.239463000000015 -92.19633460190326
sentence ('POSS', 'NEW', 'STUDENT', 'BREAK-DOWN') 0 -9.41555430000001 -93.95724839330207
sentence ('POSS', 'ARRIVE', 'CAR', 'BREAK-DOWN') 0 -7.156663219999999 -71.36833742666435
sentence ('POSS', 'ARRIVE', 'HOUSE', 'BREAK-DOWN') 0 -9.800465500000016 -97.8063642764807
sentence ('POSS', 'ARRIVE', 'STUDENT', 'BREAK-DOWN') 0 -9.976556800000012 -99.56727806787951
sentence ('POSS', 'BLAME', 'CAR', 'BREAK-DOWN') 0 -7.14359632 -71.23767170659428
sentence ('POSS', 'BLAME', 'HOUSE', 'BREAK-DOWN') 0 -9.787398600000003 -97.67569855641051
sentence ('POSS', 'BLAME', 'STUDENT', 'BREAK-DOWN') 0 -9.963489899999999 -99.43661234780932
sentence ('IX', 'NEW', 'CAR', 'BREAK-DOWN') 0 -3.834919649999989 -38.15090939286032
sentence ('IX', 'NEW', 'HOUSE', 'BREAK-DOWN') 0 -9.0154091 -89.95580794267661
sentence ('IX', 'NEW', 'STUDENT', 'BREAK-DOWN') 0 -9.191500399999995 -91.71672173407542
sentence ('IX', 'ARRIVE', 'CAR', 'BREAK-DOWN') 0 -6.699747619999997 -66.79919376743784
sentence ('IX', 'ARRIVE', 'HOUSE', 'BREAK-DOWN') 0 -9.343549900000014 -93.2372206172542
sentence ('IX', 'ARRIVE', 'STUDENT', 'BREAK-DOWN') 0 -9.51964120000001 -94.998134408653
sentence ('IX', 'BLAME', 'CAR', 'BREAK-DOWN') 0 -6.686680719999998 -66.66852804736777
sentence ('IX', 'BLAME', 'HOUSE', 'BREAK-DOWN') 0 -9.330483000000001 -93.10655489718398
sentence ('IX', 'BLAME', 'STUDENT', 'BREAK-DOWN') 0 -9.506574299999997 -94.86746868858279
Best sentence (-31.022306352086883, ('POSS', 'NEW', 'CAR', 'BREAK-DOWN'), 0, -3.122060579999996)
num_sentences 18 0.019563036010367796 seconds
0 candidates ['JOHN', 'MARY']
1 candidates []
sentence ('JOHN', 'SEE') 0 -3.0775262000000083 -30.60677713997485
sentence ('MARY', 'SEE') 0 -5.4198293000000035 -54.02983856670294
Best sentence (-30.606777139974849, ('JOHN', 'SEE'), 0, -3.0775262000000083)
num_sentences 2 0.00210873999458272 seconds
0 candidates ['JOHN', 'MARY', 'IX']
1 candidates ['JOHN', 'IX', 'GO']
2 candidates ['FRIEND', 'JOHN', 'BLAME']
3 candidates ['GO', 'HAVE', 'MARY']
4 candidates ['JOHN', 'MARY']
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sentence ('JOHN', 'JOHN', 'FRIEND', 'MARY', 'MARY') 0 -9.131958700000013 -91.13188400096574
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sentence ('JOHN', 'JOHN', 'BLAME', 'MARY', 'JOHN') 0 -8.026196900000002 -80.07426853402751
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sentence ('JOHN', 'IX', 'FRIEND', 'MARY', 'JOHN') 0 -8.653646290000012 -86.34875856716303
sentence ('JOHN', 'IX', 'FRIEND', 'MARY', 'MARY') 0 -8.215737390000015 -81.96967111666339
sentence ('JOHN', 'IX', 'JOHN', 'GO', 'JOHN') 0 -7.348446890000005 -73.29676300052054
sentence ('JOHN', 'IX', 'JOHN', 'GO', 'MARY') 0 -6.543573590000008 -65.2480315500209
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sentence ('JOHN', 'IX', 'BLAME', 'MARY', 'JOHN') 0 -7.440235290000018 -74.2146526497253
sentence ('JOHN', 'IX', 'BLAME', 'MARY', 'MARY') 0 -7.002326390000022 -69.83556519922566
sentence ('JOHN', 'GO', 'FRIEND', 'GO', 'JOHN') 0 -9.656798100000003 -96.38027451441077
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sentence ('JOHN', 'GO', 'JOHN', 'GO', 'JOHN') 0 -8.031593900000018 -80.12823642919112
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sentence ('JOHN', 'GO', 'JOHN', 'HAVE', 'JOHN') 0 -9.034392200000013 -90.15622311734522
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sentence ('JOHN', 'GO', 'JOHN', 'MARY', 'MARY') 0 -7.566356400000018 -75.47586846011406
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sentence ('JOHN', 'GO', 'BLAME', 'MARY', 'JOHN') 0 -7.893215200000014 -78.74445507839569
sentence ('JOHN', 'GO', 'BLAME', 'MARY', 'MARY') 0 -7.455306300000018 -74.36536762789605
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sentence ('MARY', 'JOHN', 'FRIEND', 'HAVE', 'MARY') 0 -10.523477700000015 -105.04707298401348
sentence ('MARY', 'JOHN', 'FRIEND', 'MARY', 'JOHN') 0 -10.437914800000016 -104.19144422778163
sentence ('MARY', 'JOHN', 'FRIEND', 'MARY', 'MARY') 0 -10.00000590000002 -99.81235677728199
sentence ('MARY', 'JOHN', 'JOHN', 'GO', 'JOHN') 0 -9.362882500000012 -93.44111966113917
sentence ('MARY', 'JOHN', 'JOHN', 'GO', 'MARY') 0 -8.558009200000015 -85.39238821063952
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sentence ('MARY', 'JOHN', 'JOHN', 'HAVE', 'MARY') 0 -9.56080750000001 -95.42037489879363
sentence ('MARY', 'JOHN', 'JOHN', 'MARY', 'JOHN') 0 -9.335553900000008 -93.16783914256175
sentence ('MARY', 'JOHN', 'JOHN', 'MARY', 'MARY') 0 -8.897645000000011 -88.7887516920621
sentence ('MARY', 'JOHN', 'BLAME', 'GO', 'JOHN') 0 -10.178922799999995 -101.601522828921
sentence ('MARY', 'JOHN', 'BLAME', 'GO', 'MARY') 0 -9.374049499999998 -93.55279137842136
sentence ('MARY', 'JOHN', 'BLAME', 'HAVE', 'JOHN') 0 -10.519187099999996 -105.00416951707517
sentence ('MARY', 'JOHN', 'BLAME', 'HAVE', 'MARY') 0 -9.7143138 -96.95543806657552
sentence ('MARY', 'JOHN', 'BLAME', 'MARY', 'JOHN') 0 -8.894244100000009 -88.75474131034376
sentence ('MARY', 'JOHN', 'BLAME', 'MARY', 'MARY') 0 -8.456335200000012 -84.37565385984412
sentence ('MARY', 'IX', 'FRIEND', 'GO', 'JOHN') 0 -11.2728125 -112.54041596205649
sentence ('MARY', 'IX', 'FRIEND', 'GO', 'MARY') 0 -10.467939200000004 -104.49168451155684
sentence ('MARY', 'IX', 'FRIEND', 'HAVE', 'JOHN') 0 -11.613076800000002 -115.94306265021065
sentence ('MARY', 'IX', 'FRIEND', 'HAVE', 'MARY') 0 -10.808203500000005 -107.894331199711
sentence ('MARY', 'IX', 'FRIEND', 'MARY', 'JOHN') 0 -10.722640600000005 -107.03870244347915
sentence ('MARY', 'IX', 'FRIEND', 'MARY', 'MARY') 0 -10.284731700000009 -102.65961499297951
sentence ('MARY', 'IX', 'JOHN', 'GO', 'JOHN') 0 -9.417441200000013 -93.98670687683682
sentence ('MARY', 'IX', 'JOHN', 'GO', 'MARY') 0 -8.612567900000016 -85.93797542633718
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sentence ('MARY', 'IX', 'JOHN', 'MARY', 'JOHN') 0 -9.390112600000009 -93.71342635825938
sentence ('MARY', 'IX', 'JOHN', 'MARY', 'MARY') 0 -8.952203700000013 -89.33433890775974
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sentence ('MARY', 'IX', 'BLAME', 'GO', 'MARY') 0 -9.989035000000001 -99.70264659411902
sentence ('MARY', 'IX', 'BLAME', 'HAVE', 'JOHN') 0 -11.1341726 -111.15402473277283
sentence ('MARY', 'IX', 'BLAME', 'HAVE', 'MARY') 0 -10.329299300000002 -103.10529328227318
sentence ('MARY', 'IX', 'BLAME', 'MARY', 'JOHN') 0 -9.509229600000012 -94.90459652604142
sentence ('MARY', 'IX', 'BLAME', 'MARY', 'MARY') 0 -9.071320700000015 -90.52550907554178
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sentence ('MARY', 'GO', 'FRIEND', 'GO', 'MARY') 0 -10.869968000000014 -108.5119758402274
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sentence ('MARY', 'GO', 'FRIEND', 'HAVE', 'MARY') 0 -11.210232300000015 -111.91462252838156
sentence ('MARY', 'GO', 'FRIEND', 'MARY', 'JOHN') 0 -11.124669400000016 -111.0589937721497
sentence ('MARY', 'GO', 'FRIEND', 'MARY', 'MARY') 0 -10.68676050000002 -106.67990632165007
sentence ('MARY', 'GO', 'JOHN', 'GO', 'JOHN') 0 -10.049637100000012 -100.30866920550724
sentence ('MARY', 'GO', 'JOHN', 'GO', 'MARY') 0 -9.244763800000015 -92.2599377550076
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sentence ('MARY', 'GO', 'JOHN', 'HAVE', 'MARY') 0 -10.24756210000001 -102.2879244431617
sentence ('MARY', 'GO', 'JOHN', 'MARY', 'JOHN') 0 -10.022308500000008 -100.03538868692982
sentence ('MARY', 'GO', 'JOHN', 'MARY', 'MARY') 0 -9.584399600000012 -95.65630123643018
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sentence ('MARY', 'GO', 'BLAME', 'GO', 'MARY') 0 -10.391063800000012 -103.72293792278957
sentence ('MARY', 'GO', 'BLAME', 'HAVE', 'JOHN') 0 -11.53620140000001 -115.17431606144338
sentence ('MARY', 'GO', 'BLAME', 'HAVE', 'MARY') 0 -10.731328100000013 -107.12558461094373
sentence ('MARY', 'GO', 'BLAME', 'MARY', 'JOHN') 0 -9.911258400000023 -98.92488785471197
sentence ('MARY', 'GO', 'BLAME', 'MARY', 'MARY') 0 -9.473349500000026 -94.54580040421233
sentence ('IX', 'JOHN', 'FRIEND', 'GO', 'JOHN') 0 -10.64654250000001 -106.27772106348509
sentence ('IX', 'JOHN', 'FRIEND', 'GO', 'MARY') 0 -9.841669200000013 -98.22898961298544
sentence ('IX', 'JOHN', 'FRIEND', 'HAVE', 'JOHN') 0 -10.98680680000001 -109.68036775163925
sentence ('IX', 'JOHN', 'FRIEND', 'HAVE', 'MARY') 0 -10.181933500000014 -101.63163630113961
sentence ('IX', 'JOHN', 'FRIEND', 'MARY', 'JOHN') 0 -10.096370600000014 -100.77600754490776
sentence ('IX', 'JOHN', 'FRIEND', 'MARY', 'MARY') 0 -9.658461700000018 -96.39692009440812
sentence ('IX', 'JOHN', 'JOHN', 'GO', 'JOHN') 0 -9.02133830000001 -90.0256829782653
sentence ('IX', 'JOHN', 'JOHN', 'GO', 'MARY') 0 -8.216465000000014 -81.97695152776565
sentence ('IX', 'JOHN', 'JOHN', 'HAVE', 'JOHN') 0 -10.024136600000006 -100.0536696664194
sentence ('IX', 'JOHN', 'JOHN', 'HAVE', 'MARY') 0 -9.219263300000009 -92.00493821591975
sentence ('IX', 'JOHN', 'JOHN', 'MARY', 'JOHN') 0 -8.994009700000007 -89.75240245968787
sentence ('IX', 'JOHN', 'JOHN', 'MARY', 'MARY') 0 -8.55610080000001 -85.37331500918823
sentence ('IX', 'JOHN', 'BLAME', 'GO', 'JOHN') 0 -9.837378599999994 -98.18608614604713
sentence ('IX', 'JOHN', 'BLAME', 'GO', 'MARY') 0 -9.032505299999997 -90.13735469554749
sentence ('IX', 'JOHN', 'BLAME', 'HAVE', 'JOHN') 0 -10.177642899999995 -101.5887328342013
sentence ('IX', 'JOHN', 'BLAME', 'HAVE', 'MARY') 0 -9.372769599999998 -93.54000138370165
sentence ('IX', 'JOHN', 'BLAME', 'MARY', 'JOHN') 0 -8.552699900000007 -85.33930462746989
sentence ('IX', 'JOHN', 'BLAME', 'MARY', 'MARY') 0 -8.114791000000011 -80.96021717697025
sentence ('IX', 'IX', 'FRIEND', 'GO', 'JOHN') 0 -11.188810000000004 -111.70039627918266
sentence ('IX', 'IX', 'FRIEND', 'GO', 'MARY') 0 -10.383936700000007 -103.65166482868301
sentence ('IX', 'IX', 'FRIEND', 'HAVE', 'JOHN') 0 -11.529074300000005 -115.10304296733682
sentence ('IX', 'IX', 'FRIEND', 'HAVE', 'MARY') 0 -10.724201000000008 -107.05431151683717
sentence ('IX', 'IX', 'FRIEND', 'MARY', 'JOHN') 0 -10.638638100000009 -106.19868276060532
sentence ('IX', 'IX', 'FRIEND', 'MARY', 'MARY') 0 -10.200729200000012 -101.81959531010568
sentence ('IX', 'IX', 'JOHN', 'GO', 'JOHN') 0 -9.333438700000016 -93.14668719396298
sentence ('IX', 'IX', 'JOHN', 'GO', 'MARY') 0 -8.52856540000002 -85.09795574346333
sentence ('IX', 'IX', 'JOHN', 'HAVE', 'JOHN') 0 -10.336237000000011 -103.17467388211708
sentence ('IX', 'IX', 'JOHN', 'HAVE', 'MARY') 0 -9.531363700000014 -95.12594243161743
sentence ('IX', 'IX', 'JOHN', 'MARY', 'JOHN') 0 -9.306110100000012 -92.87340667538555
sentence ('IX', 'IX', 'JOHN', 'MARY', 'MARY') 0 -8.868201200000016 -88.49431922488591
sentence ('IX', 'IX', 'BLAME', 'GO', 'JOHN') 0 -10.709905800000001 -106.91135836174483
sentence ('IX', 'IX', 'BLAME', 'GO', 'MARY') 0 -9.905032500000004 -98.86262691124519
sentence ('IX', 'IX', 'BLAME', 'HAVE', 'JOHN') 0 -11.050170100000003 -110.314005049899
sentence ('IX', 'IX', 'BLAME', 'HAVE', 'MARY') 0 -10.245296800000006 -102.26527359939935
sentence ('IX', 'IX', 'BLAME', 'MARY', 'JOHN') 0 -9.425227100000015 -94.06457684316759
sentence ('IX', 'IX', 'BLAME', 'MARY', 'MARY') 0 -8.987318200000018 -89.68548939266795
sentence ('IX', 'GO', 'FRIEND', 'GO', 'JOHN') 0 -11.196499800000012 -111.77729760785319
sentence ('IX', 'GO', 'FRIEND', 'GO', 'MARY') 0 -10.391626500000015 -103.72856615735354
sentence ('IX', 'GO', 'FRIEND', 'HAVE', 'JOHN') 0 -11.536764100000013 -115.17994429600735
sentence ('IX', 'GO', 'FRIEND', 'HAVE', 'MARY') 0 -10.731890800000016 -107.1312128455077
sentence ('IX', 'GO', 'FRIEND', 'MARY', 'JOHN') 0 -10.646327900000017 -106.27558408927585
sentence ('IX', 'GO', 'FRIEND', 'MARY', 'MARY') 0 -10.20841900000002 -101.89649663877621
sentence ('IX', 'GO', 'JOHN', 'GO', 'JOHN') 0 -9.571295600000013 -95.52525952263339
sentence ('IX', 'GO', 'JOHN', 'GO', 'MARY') 0 -8.766422300000016 -87.47652807213375
sentence ('IX', 'GO', 'JOHN', 'HAVE', 'JOHN') 0 -10.574093900000008 -105.55324621078749
sentence ('IX', 'GO', 'JOHN', 'HAVE', 'MARY') 0 -9.769220600000011 -97.50451476028785
sentence ('IX', 'GO', 'JOHN', 'MARY', 'JOHN') 0 -9.54396700000001 -95.25197900405597
sentence ('IX', 'GO', 'JOHN', 'MARY', 'MARY') 0 -9.106058100000013 -90.87289155355633
sentence ('IX', 'GO', 'BLAME', 'GO', 'JOHN') 0 -10.71759560000001 -106.98825969041536
sentence ('IX', 'GO', 'BLAME', 'GO', 'MARY') 0 -9.912722300000013 -98.93952823991572
sentence ('IX', 'GO', 'BLAME', 'HAVE', 'JOHN') 0 -11.057859900000011 -110.39090637856953
sentence ('IX', 'GO', 'BLAME', 'HAVE', 'MARY') 0 -10.252986600000014 -102.34217492806988
sentence ('IX', 'GO', 'BLAME', 'MARY', 'JOHN') 0 -9.432916900000023 -94.14147817183812
sentence ('IX', 'GO', 'BLAME', 'MARY', 'MARY') 0 -8.995008000000027 -89.76239072133846
Best sentence (-65.248031550020897, ('JOHN', 'IX', 'JOHN', 'GO', 'MARY'), 0, -6.543573590000008)
num_sentences 162 0.19453018499189056 seconds
0 candidates ['IX', 'MARY', 'WOMAN']
1 candidates ['LOVE', 'ARRIVE', 'HOMEWORK']
sentence ('IX', 'LOVE') 0 -4.432703099999998 -44.09719736301892
sentence ('IX', 'ARRIVE') 0 -3.8126669999999905 -37.89684276681648
sentence ('IX', 'HOMEWORK') 0 -4.731304399999999 -47.08322127943207
sentence ('MARY', 'LOVE') 0 -3.2391112000000106 -32.1613031421237
sentence ('MARY', 'ARRIVE') 0 -4.291008500000004 -42.680282545921266
sentence ('MARY', 'HOMEWORK') 0 -5.209645900000012 -51.866661058536856
sentence ('WOMAN', 'LOVE') 0 -5.324770200000003 -53.017909654376034
sentence ('WOMAN', 'ARRIVE') 0 -3.469842099999994 -34.46863505817357
sentence ('WOMAN', 'HOMEWORK') 0 -5.623371500000005 -56.00393357078919
Best sentence (-32.1613031421237, ('MARY', 'LOVE'), 0, -3.2391112000000106)
num_sentences 9 0.0060642669996013865 seconds
0 candidates ['IX', 'JOHN', 'MARY']
1 candidates ['CAR', 'BOX', 'ARRIVE']
2 candidates ['IX', 'CAR', 'MARY']
3 candidates ['MARY', 'IX', 'JOHN']
4 candidates ['BOX', 'IX', 'CAR']
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sentence ('IX', 'CAR', 'IX', 'MARY', 'IX') 0 -9.17543839999999 -91.53683739402268
sentence ('IX', 'CAR', 'IX', 'MARY', 'CAR') 0 -10.192145499999995 -101.70391648175573
sentence ('IX', 'CAR', 'IX', 'IX', 'BOX') 0 -9.91702020000001 -98.95262518785763
sentence ('IX', 'CAR', 'IX', 'IX', 'IX') 0 -6.9942657 -69.725111573361
sentence ('IX', 'CAR', 'IX', 'IX', 'CAR') 0 -10.273562400000003 -102.51808666109403
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sentence ('IX', 'CAR', 'IX', 'JOHN', 'IX') 0 -8.600044490000002 -85.78290688291162
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sentence ('IX', 'CAR', 'CAR', 'MARY', 'BOX') 0 -10.228128100000006 -102.06371075204886
sentence ('IX', 'CAR', 'CAR', 'MARY', 'IX') 0 -9.662064 -96.40310113755228
sentence ('IX', 'CAR', 'CAR', 'MARY', 'CAR') 0 -10.678771100000006 -106.57018022528533
sentence ('IX', 'CAR', 'CAR', 'IX', 'BOX') 0 -10.009306800000005 -99.87549893138707
sentence ('IX', 'CAR', 'CAR', 'IX', 'IX') 0 -8.768968200000003 -87.47214431689054
sentence ('IX', 'CAR', 'CAR', 'IX', 'CAR') 0 -10.365848999999997 -103.44096040462347
sentence ('IX', 'CAR', 'CAR', 'JOHN', 'BOX') 0 -9.448425040000004 -94.26668874093765
sentence ('IX', 'CAR', 'CAR', 'JOHN', 'IX') 0 -9.316837190000001 -92.9508416264411
sentence ('IX', 'CAR', 'CAR', 'JOHN', 'CAR') 0 -10.5421884 -105.20436181417409
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sentence ('IX', 'CAR', 'MARY', 'MARY', 'IX') 0 -9.394259700000006 -93.72506193817554
sentence ('IX', 'CAR', 'MARY', 'MARY', 'CAR') 0 -10.410966800000011 -103.89214102590859
sentence ('IX', 'CAR', 'MARY', 'IX', 'BOX') 0 -9.74150250000001 -97.19745973201033
sentence ('IX', 'CAR', 'MARY', 'IX', 'IX') 0 -8.501163900000009 -84.7941051175138
sentence ('IX', 'CAR', 'MARY', 'IX', 'CAR') 0 -10.098044700000003 -100.76292120524673
sentence ('IX', 'CAR', 'MARY', 'JOHN', 'BOX') 0 -8.813656340000009 -87.91900554156089
sentence ('IX', 'CAR', 'MARY', 'JOHN', 'IX') 0 -8.682068490000006 -86.60315842706434
sentence ('IX', 'CAR', 'MARY', 'JOHN', 'CAR') 0 -9.907419700000005 -98.85667861479735
sentence ('IX', 'BOX', 'IX', 'MARY', 'BOX') 0 -10.392697499999997 -103.70941464176587
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Best sentence (-56.755040389726332, ('JOHN', 'ARRIVE', 'IX', 'IX', 'IX'), 0, -5.697255200000001)
num_sentences 243 0.3210633309936384 seconds
0 candidates ['MARY', 'VISIT']
1 candidates ['BUY', 'LOVE', 'ARRIVE']
2 candidates ['IX', 'CAR', 'GO']
3 candidates ['BLAME', 'JOHN', 'CAR']
4 candidates ['GO', 'IX', 'JOHN']
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sentence ('VISIT', 'ARRIVE', 'CAR', 'BLAME', 'IX') 0 -11.469317899999993 -114.49223622316951
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sentence ('VISIT', 'ARRIVE', 'CAR', 'JOHN', 'JOHN') 0 -11.768993000000009 -117.48899029297046
sentence ('VISIT', 'ARRIVE', 'CAR', 'CAR', 'GO') 0 -12.538887700000004 -125.18793033075318
sentence ('VISIT', 'ARRIVE', 'CAR', 'CAR', 'IX') 0 -11.389938900000004 -113.698447193733
sentence ('VISIT', 'ARRIVE', 'CAR', 'CAR', 'JOHN') 0 -12.173380000000009 -121.5328603111047
sentence ('VISIT', 'ARRIVE', 'GO', 'BLAME', 'GO') 0 -12.533669899999978 -125.13575403220149
sentence ('VISIT', 'ARRIVE', 'GO', 'BLAME', 'IX') 0 -11.384721099999979 -113.64627089518133
sentence ('VISIT', 'ARRIVE', 'GO', 'BLAME', 'JOHN') 0 -12.168162199999983 -121.48068401255301
sentence ('VISIT', 'ARRIVE', 'GO', 'JOHN', 'GO') 0 -11.387369899999996 -113.67275498463081
sentence ('VISIT', 'ARRIVE', 'GO', 'JOHN', 'IX') 0 -10.692310989999996 -106.72217074761065
sentence ('VISIT', 'ARRIVE', 'GO', 'JOHN', 'JOHN') 0 -11.684396199999995 -116.64302496498227
sentence ('VISIT', 'ARRIVE', 'GO', 'CAR', 'GO') 0 -12.45429089999999 -124.341965002765
sentence ('VISIT', 'ARRIVE', 'GO', 'CAR', 'IX') 0 -11.30534209999999 -112.85248186574482
sentence ('VISIT', 'ARRIVE', 'GO', 'CAR', 'JOHN') 0 -12.088783199999995 -120.68689498311652
Best sentence (-79.082905984393264, ('MARY', 'LOVE', 'IX', 'JOHN', 'IX'), 0, -7.92838629000002)
num_sentences 162 0.196666023999569 seconds
0 candidates ['JOHN', 'SOMETHING-ONE', 'IX']
1 candidates ['GIVE', 'STUDENT', 'BOOK']
2 candidates ['BOOK']
sentence ('JOHN', 'GIVE', 'BOOK') 0 -4.276982700000005 -42.55131759617106
sentence ('JOHN', 'STUDENT', 'BOOK') 0 -5.367702400000013 -53.45823983668326
sentence ('JOHN', 'BOOK', 'BOOK') 0 -5.111538899999999 -50.8966057670136
sentence ('SOMETHING-ONE', 'GIVE', 'BOOK') 0 -5.628646200000006 -56.06799337244848
sentence ('SOMETHING-ONE', 'STUDENT', 'BOOK') 0 -4.136294300000017 -41.144199612960705
sentence ('SOMETHING-ONE', 'BOOK', 'BOOK') 0 -5.955230299999997 -59.33356054329097
sentence ('IX', 'GIVE', 'BOOK') 0 -5.230171800000008 -52.08328478252834
sentence ('IX', 'STUDENT', 'BOOK') 0 -6.124372500000007 -61.02501702304044
sentence ('IX', 'BOOK', 'BOOK') 0 -5.147509900000003 -51.25639195337088
Best sentence (-41.144199612960705, ('SOMETHING-ONE', 'STUDENT', 'BOOK'), 0, -4.136294300000017)
num_sentences 9 0.00801494599727448 seconds
0 candidates ['JOHN', 'IX', 'MARY']
1 candidates ['BUY', 'ARRIVE', 'LOVE']
2 candidates ['WHAT', 'BOX', 'CAR']
3 candidates ['BOX', 'JOHN', 'WHAT']
4 candidates ['BOOK', 'CHOCOLATE', 'BLAME']
sentence ('JOHN', 'BUY', 'WHAT', 'BOX', 'BOOK') 0 -7.045936700000013 -70.23762966357033
sentence ('JOHN', 'BUY', 'WHAT', 'BOX', 'CHOCOLATE') 0 -8.43704430000001 -84.14870820558855
sentence ('JOHN', 'BUY', 'WHAT', 'BOX', 'BLAME') 0 -7.630496900000011 -76.08323915813077
sentence ('JOHN', 'BUY', 'WHAT', 'JOHN', 'BOOK') 0 -6.058698199999995 -60.365246349665014
sentence ('JOHN', 'BUY', 'WHAT', 'JOHN', 'CHOCOLATE') 0 -7.449805800000007 -74.27632489168337
sentence ('JOHN', 'BUY', 'WHAT', 'JOHN', 'BLAME') 0 -6.312998699999994 -62.90825884422545
sentence ('JOHN', 'BUY', 'WHAT', 'WHAT', 'BOOK') 0 -5.10492425999999 -50.82750931253532
sentence ('JOHN', 'BUY', 'WHAT', 'WHAT', 'CHOCOLATE') 0 -7.642080800000002 -76.19907725455369
sentence ('JOHN', 'BUY', 'WHAT', 'WHAT', 'BLAME') 0 -6.835533400000003 -68.13360820709589
sentence ('JOHN', 'BUY', 'BOX', 'BOX', 'BOOK') 0 -9.541646400000005 -95.19472928284534
sentence ('JOHN', 'BUY', 'BOX', 'BOX', 'CHOCOLATE') 0 -10.932754000000003 -109.10580782486356
sentence ('JOHN', 'BUY', 'BOX', 'BOX', 'BLAME') 0 -10.126206600000003 -101.04033877740578
sentence ('JOHN', 'BUY', 'BOX', 'JOHN', 'BOOK') 0 -8.58016520000001 -85.57991896894026
sentence ('JOHN', 'BUY', 'BOX', 'JOHN', 'CHOCOLATE') 0 -9.971272800000008 -99.49099751095848
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sentence ('JOHN', 'BUY', 'BOX', 'WHAT', 'BOOK') 0 -7.600633959999996 -75.78460893181048
sentence ('JOHN', 'BUY', 'BOX', 'WHAT', 'CHOCOLATE') 0 -10.137790500000008 -101.15617687382884
sentence ('JOHN', 'BUY', 'BOX', 'WHAT', 'BLAME') 0 -9.331243100000009 -93.09070782637106
sentence ('JOHN', 'BUY', 'CAR', 'BOX', 'BOOK') 0 -6.739690300000007 -67.17517104984756
sentence ('JOHN', 'BUY', 'CAR', 'BOX', 'CHOCOLATE') 0 -8.130797900000005 -81.08624959186578
sentence ('JOHN', 'BUY', 'CAR', 'BOX', 'BLAME') 0 -7.324250500000005 -73.020780544408
sentence ('JOHN', 'BUY', 'CAR', 'JOHN', 'BOOK') 0 -5.778209100000012 -57.56036073594249
sentence ('JOHN', 'BUY', 'CAR', 'JOHN', 'CHOCOLATE') 0 -7.16931670000001 -71.47143927796071
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sentence ('JOHN', 'BUY', 'CAR', 'WHAT', 'BOOK') 0 -4.798677859999998 -47.7650506988127
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sentence ('JOHN', 'BUY', 'CAR', 'WHAT', 'BLAME') 0 -6.529287000000011 -65.07114959337328
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sentence ('JOHN', 'ARRIVE', 'WHAT', 'BOX', 'CHOCOLATE') 0 -10.275173600000002 -102.52993033796365
sentence ('JOHN', 'ARRIVE', 'WHAT', 'BOX', 'BLAME') 0 -9.468626200000003 -94.46446129050587
sentence ('JOHN', 'ARRIVE', 'WHAT', 'JOHN', 'BOOK') 0 -7.8968275000000006 -78.74646848204026
sentence ('JOHN', 'ARRIVE', 'WHAT', 'JOHN', 'CHOCOLATE') 0 -9.287935100000013 -92.65754702405862
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Best sentence (-47.765050698812701, ('JOHN', 'BUY', 'CAR', 'WHAT', 'BOOK'), 0, -4.798677859999998)
num_sentences 243 0.32478278799680993 seconds
0 candidates ['JOHN', 'MARY', 'IX']
1 candidates ['BUY', 'NEW', 'STUDENT']
2 candidates ['YESTERDAY', 'CHICAGO', 'VISIT']
3 candidates ['WHAT', 'CAR', 'HOUSE']
4 candidates ['BOOK', 'COAT', 'BROTHER']
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sentence ('IX', 'NEW', 'YESTERDAY', 'WHAT', 'COAT') 0 -10.549666599999995 -105.30142166751205
sentence ('IX', 'NEW', 'YESTERDAY', 'WHAT', 'BROTHER') 0 -11.237367999999975 -112.17843620031164
sentence ('IX', 'NEW', 'YESTERDAY', 'CAR', 'BOOK') 0 -9.577589999999987 -95.58065820498491
sentence ('IX', 'NEW', 'YESTERDAY', 'CAR', 'COAT') 0 -10.748545099999987 -107.29021155589109
sentence ('IX', 'NEW', 'YESTERDAY', 'CAR', 'BROTHER') 0 -11.436246499999982 -114.16722608869084
sentence ('IX', 'NEW', 'YESTERDAY', 'HOUSE', 'BOOK') 0 -9.833654199999984 -98.14130246656343
sentence ('IX', 'NEW', 'YESTERDAY', 'HOUSE', 'COAT') 0 -11.004609299999998 -109.85085581746976
sentence ('IX', 'NEW', 'YESTERDAY', 'HOUSE', 'BROTHER') 0 -11.692310699999979 -116.72787035026936
sentence ('IX', 'NEW', 'CHICAGO', 'WHAT', 'BOOK') 0 -8.894622459999994 -88.75098124666478
sentence ('IX', 'NEW', 'CHICAGO', 'WHAT', 'COAT') 0 -11.211626500000008 -111.9210239975711
sentence ('IX', 'NEW', 'CHICAGO', 'WHAT', 'BROTHER') 0 -11.899327899999989 -118.7980385303707
sentence ('IX', 'NEW', 'CHICAGO', 'CAR', 'BOOK') 0 -10.278540699999994 -102.5901685350439
sentence ('IX', 'NEW', 'CHICAGO', 'CAR', 'COAT') 0 -11.449495799999994 -114.29972188595009
sentence ('IX', 'NEW', 'CHICAGO', 'CAR', 'BROTHER') 0 -12.137197199999989 -121.17673641874984
sentence ('IX', 'NEW', 'CHICAGO', 'HOUSE', 'BOOK') 0 -10.53460489999999 -105.15081279662243
sentence ('IX', 'NEW', 'CHICAGO', 'HOUSE', 'COAT') 0 -11.705560000000006 -116.86036614752875
sentence ('IX', 'NEW', 'CHICAGO', 'HOUSE', 'BROTHER') 0 -12.393261399999986 -123.73738068032836
sentence ('IX', 'NEW', 'VISIT', 'WHAT', 'BOOK') 0 -7.7725965599999824 -77.53072243299685
sentence ('IX', 'NEW', 'VISIT', 'WHAT', 'COAT') 0 -10.089600599999997 -100.70076518390319
sentence ('IX', 'NEW', 'VISIT', 'WHAT', 'BROTHER') 0 -10.777301999999978 -107.57777971670278
sentence ('IX', 'NEW', 'VISIT', 'CAR', 'BOOK') 0 -9.741446799999991 -97.21922972137608
sentence ('IX', 'NEW', 'VISIT', 'CAR', 'COAT') 0 -10.912401899999992 -108.92878307228227
sentence ('IX', 'NEW', 'VISIT', 'CAR', 'BROTHER') 0 -11.600103299999986 -115.805797605082
sentence ('IX', 'NEW', 'VISIT', 'HOUSE', 'BOOK') 0 -9.997510999999989 -99.7798739829546
sentence ('IX', 'NEW', 'VISIT', 'HOUSE', 'COAT') 0 -11.168466100000003 -111.48942733386093
sentence ('IX', 'NEW', 'VISIT', 'HOUSE', 'BROTHER') 0 -11.856167499999984 -118.36644186666052
sentence ('IX', 'STUDENT', 'YESTERDAY', 'WHAT', 'BOOK') 0 -7.383336659999998 -73.63812069746935
sentence ('IX', 'STUDENT', 'YESTERDAY', 'WHAT', 'COAT') 0 -11.217517 -111.97992644837556
sentence ('IX', 'STUDENT', 'YESTERDAY', 'WHAT', 'BROTHER') 0 -11.905218399999995 -118.85694098117531
sentence ('IX', 'STUDENT', 'YESTERDAY', 'CAR', 'BOOK') 0 -10.245440399999993 -102.25916298584843
sentence ('IX', 'STUDENT', 'YESTERDAY', 'CAR', 'COAT') 0 -11.416395499999993 -113.96871633675462
sentence ('IX', 'STUDENT', 'YESTERDAY', 'CAR', 'BROTHER') 0 -12.104096899999988 -120.84573086955436
sentence ('IX', 'STUDENT', 'YESTERDAY', 'HOUSE', 'BOOK') 0 -10.50150459999999 -104.81980724742695
sentence ('IX', 'STUDENT', 'YESTERDAY', 'HOUSE', 'COAT') 0 -11.672459700000005 -116.52936059833328
sentence ('IX', 'STUDENT', 'YESTERDAY', 'HOUSE', 'BROTHER') 0 -12.360161099999985 -123.40637513113289
sentence ('IX', 'STUDENT', 'CHICAGO', 'WHAT', 'BOOK') 0 -9.56247286 -95.4294860275283
sentence ('IX', 'STUDENT', 'CHICAGO', 'WHAT', 'COAT') 0 -11.879476900000014 -118.59952877843463
sentence ('IX', 'STUDENT', 'CHICAGO', 'WHAT', 'BROTHER') 0 -12.567178299999995 -125.47654331123422
sentence ('IX', 'STUDENT', 'CHICAGO', 'CAR', 'BOOK') 0 -10.9463911 -109.26867331590744
sentence ('IX', 'STUDENT', 'CHICAGO', 'CAR', 'COAT') 0 -12.1173462 -120.97822666681363
sentence ('IX', 'STUDENT', 'CHICAGO', 'CAR', 'BROTHER') 0 -12.805047599999995 -127.85524119961336
sentence ('IX', 'STUDENT', 'CHICAGO', 'HOUSE', 'BOOK') 0 -11.202455299999997 -111.82931757748595
sentence ('IX', 'STUDENT', 'CHICAGO', 'HOUSE', 'COAT') 0 -12.373410400000012 -123.53887092839229
sentence ('IX', 'STUDENT', 'CHICAGO', 'HOUSE', 'BROTHER') 0 -13.061111799999992 -130.4158854611919
sentence ('IX', 'STUDENT', 'VISIT', 'WHAT', 'BOOK') 0 -8.440446959999989 -84.20922721386037
sentence ('IX', 'STUDENT', 'VISIT', 'WHAT', 'COAT') 0 -10.757451000000003 -107.3792699647667
sentence ('IX', 'STUDENT', 'VISIT', 'WHAT', 'BROTHER') 0 -11.445152399999984 -114.25628449756631
sentence ('IX', 'STUDENT', 'VISIT', 'CAR', 'BOOK') 0 -10.409297199999997 -103.8977345022396
sentence ('IX', 'STUDENT', 'VISIT', 'CAR', 'COAT') 0 -11.580252299999998 -115.60728785314579
sentence ('IX', 'STUDENT', 'VISIT', 'CAR', 'BROTHER') 0 -12.267953699999993 -122.48430238594553
sentence ('IX', 'STUDENT', 'VISIT', 'HOUSE', 'BOOK') 0 -10.665361399999995 -106.45837876381812
sentence ('IX', 'STUDENT', 'VISIT', 'HOUSE', 'COAT') 0 -11.83631650000001 -118.16793211472445
sentence ('IX', 'STUDENT', 'VISIT', 'HOUSE', 'BROTHER') 0 -12.52401789999999 -125.04494664752406
Best sentence (-23.002133270576596, ('JOHN', 'BUY', 'YESTERDAY', 'WHAT', 'BOOK'), 0, -2.31973936)
num_sentences 243 0.298347500996897 seconds
0 candidates ['LOVE', 'ARRIVE', 'GIVE']
1 candidates ['JOHN', 'MARY', 'IX']
2 candidates []
sentence ('LOVE', 'JOHN', 'WHO') 0 -2.311486380000005 -22.916768224039973
sentence ('LOVE', 'MARY', 'WHO') 0 -4.963151100000005 -49.43342642227958
sentence ('LOVE', 'IX', 'WHO') 0 -5.530165199999999 -55.10358956627313
sentence ('ARRIVE', 'JOHN', 'WHO') 0 -6.267168980000008 -62.47362571899058
sentence ('ARRIVE', 'MARY', 'WHO') 0 -6.954035200000007 -69.34229891723018
sentence ('ARRIVE', 'IX', 'WHO') 0 -6.489093799999992 -64.69290706122364
sentence ('GIVE', 'JOHN', 'WHO') 0 -6.339826680000002 -63.20083115713887
sentence ('GIVE', 'MARY', 'WHO') 0 -6.758182200000007 -67.38439735537855
sentence ('GIVE', 'IX', 'WHO') 0 -5.876348900000011 -58.566086499372176
Best sentence (-22.916768224039973, ('LOVE', 'JOHN', 'WHO'), 0, -2.311486380000005)
num_sentences 9 0.008340942003997043 seconds
0 candidates []
1 candidates ['IX', 'MARY', 'JOHN']
2 candidates ['VISIT', 'MARY', 'GO']
3 candidates ['LOVE', 'MARY', 'ARRIVE']
4 candidates ['MARY', 'LOVE', 'IX']
sentence ('JOHN', 'IX', 'VISIT', 'LOVE', 'MARY') 0 -6.822070089999997 -68.01589360822405
sentence ('JOHN', 'IX', 'VISIT', 'LOVE', 'LOVE') 0 -8.902748590000002 -88.82269031720882
sentence ('JOHN', 'IX', 'VISIT', 'LOVE', 'IX') 0 -7.704797289999988 -76.84319375671056
sentence ('JOHN', 'IX', 'VISIT', 'MARY', 'MARY') 0 -6.755820790000001 -67.35342978336902
sentence ('JOHN', 'IX', 'VISIT', 'MARY', 'LOVE') 0 -5.909656690000006 -58.8918004923538
sentence ('JOHN', 'IX', 'VISIT', 'MARY', 'IX') 0 -6.777252989999994 -67.56777993185557
sentence ('JOHN', 'IX', 'VISIT', 'ARRIVE', 'MARY') 0 -8.038893689999995 -80.18416595393782
sentence ('JOHN', 'IX', 'VISIT', 'ARRIVE', 'LOVE') 0 -8.86466299 -88.4418706629226
sentence ('JOHN', 'IX', 'VISIT', 'ARRIVE', 'IX') 0 -8.060325889999987 -80.39851610242435
sentence ('JOHN', 'IX', 'MARY', 'LOVE', 'MARY') 0 -5.5520586899999955 -55.31578678452501
sentence ('JOHN', 'IX', 'MARY', 'LOVE', 'LOVE') 0 -7.63273719 -76.12258349350978
sentence ('JOHN', 'IX', 'MARY', 'LOVE', 'IX') 0 -6.434785890000001 -64.14308693301167
sentence ('JOHN', 'IX', 'MARY', 'MARY', 'MARY') 0 -7.389649890000015 -73.69172795967015
sentence ('JOHN', 'IX', 'MARY', 'MARY', 'LOVE') 0 -6.543485790000005 -65.23009866865478
sentence ('JOHN', 'IX', 'MARY', 'MARY', 'IX') 0 -7.411082090000008 -73.90607810815668
sentence ('JOHN', 'IX', 'MARY', 'ARRIVE', 'MARY') 0 -7.749900089999997 -77.29423713023881
sentence ('JOHN', 'IX', 'MARY', 'ARRIVE', 'LOVE') 0 -8.575669390000002 -85.55194183922359
sentence ('JOHN', 'IX', 'MARY', 'ARRIVE', 'IX') 0 -7.7713322899999895 -77.50858727872536
sentence ('JOHN', 'IX', 'GO', 'LOVE', 'MARY') 0 -6.716283990000008 -66.95804080651521
sentence ('JOHN', 'IX', 'GO', 'LOVE', 'LOVE') 0 -8.796962490000013 -87.76483751549999
sentence ('JOHN', 'IX', 'GO', 'LOVE', 'IX') 0 -7.599011189999999 -75.78534095500173
sentence ('JOHN', 'IX', 'GO', 'MARY', 'MARY') 0 -7.57285739000001 -75.52380398166018
sentence ('JOHN', 'IX', 'GO', 'MARY', 'LOVE') 0 -6.72669329 -67.0621746906448
sentence ('JOHN', 'IX', 'GO', 'MARY', 'IX') 0 -7.5942895900000025 -75.73815413014671
sentence ('JOHN', 'IX', 'GO', 'ARRIVE', 'MARY') 0 -7.933107590000006 -79.12631315222899
sentence ('JOHN', 'IX', 'GO', 'ARRIVE', 'LOVE') 0 -8.75887689000001 -87.38401786121376
sentence ('JOHN', 'IX', 'GO', 'ARRIVE', 'IX') 0 -7.954539789999998 -79.34066330071552
sentence ('JOHN', 'MARY', 'VISIT', 'LOVE', 'MARY') 0 -6.735291400000008 -67.14810812937182
sentence ('JOHN', 'MARY', 'VISIT', 'LOVE', 'LOVE') 0 -8.815969900000013 -87.9549048383566
sentence ('JOHN', 'MARY', 'VISIT', 'LOVE', 'IX') 0 -7.618018599999999 -75.97540827785834
sentence ('JOHN', 'MARY', 'VISIT', 'MARY', 'MARY') 0 -6.669042100000013 -66.4856443045168
sentence ('JOHN', 'MARY', 'VISIT', 'MARY', 'LOVE') 0 -5.822878000000017 -58.02401501350157
sentence ('JOHN', 'MARY', 'VISIT', 'MARY', 'IX') 0 -6.690474300000005 -66.69999445300334
sentence ('JOHN', 'MARY', 'VISIT', 'ARRIVE', 'MARY') 0 -7.952115000000006 -79.31638047508558
sentence ('JOHN', 'MARY', 'VISIT', 'ARRIVE', 'LOVE') 0 -8.77788430000001 -87.57408518407036
sentence ('JOHN', 'MARY', 'VISIT', 'ARRIVE', 'IX') 0 -7.973547199999999 -79.53073062357213
sentence ('JOHN', 'MARY', 'MARY', 'LOVE', 'MARY') 0 -6.028406400000009 -60.07926530567279
sentence ('JOHN', 'MARY', 'MARY', 'LOVE', 'LOVE') 0 -8.109084900000013 -80.88606201465757
sentence ('JOHN', 'MARY', 'MARY', 'LOVE', 'IX') 0 -6.911133600000014 -68.90656545415946
sentence ('JOHN', 'MARY', 'MARY', 'MARY', 'MARY') 0 -7.8659976000000285 -78.45520648081794
sentence ('JOHN', 'MARY', 'MARY', 'MARY', 'LOVE') 0 -7.0198335000000185 -69.99357718980256
sentence ('JOHN', 'MARY', 'MARY', 'MARY', 'IX') 0 -7.887429800000021 -78.66955662930447
sentence ('JOHN', 'MARY', 'MARY', 'ARRIVE', 'MARY') 0 -8.22624780000001 -82.0577156513866
sentence ('JOHN', 'MARY', 'MARY', 'ARRIVE', 'LOVE') 0 -9.052017100000015 -90.31542036037138
sentence ('JOHN', 'MARY', 'MARY', 'ARRIVE', 'IX') 0 -8.247680000000003 -82.27206579987313
sentence ('JOHN', 'MARY', 'GO', 'LOVE', 'MARY') 0 -7.192631700000007 -71.72151932766286
sentence ('JOHN', 'MARY', 'GO', 'LOVE', 'LOVE') 0 -9.273310200000012 -92.52831603664764
sentence ('JOHN', 'MARY', 'GO', 'LOVE', 'IX') 0 -8.075358899999998 -80.54881947614938
sentence ('JOHN', 'MARY', 'GO', 'MARY', 'MARY') 0 -8.049205100000023 -80.28728250280795
sentence ('JOHN', 'MARY', 'GO', 'MARY', 'LOVE') 0 -7.203041000000013 -71.82565321179258
sentence ('JOHN', 'MARY', 'GO', 'MARY', 'IX') 0 -8.070637300000016 -80.5016326512945
sentence ('JOHN', 'MARY', 'GO', 'ARRIVE', 'MARY') 0 -8.409455300000019 -83.88979167337678
sentence ('JOHN', 'MARY', 'GO', 'ARRIVE', 'LOVE') 0 -9.235224600000024 -92.14749638236155
sentence ('JOHN', 'MARY', 'GO', 'ARRIVE', 'IX') 0 -8.430887500000011 -84.10414182186331
sentence ('JOHN', 'JOHN', 'VISIT', 'LOVE', 'MARY') 0 -7.127458700000005 -71.06979233001582
sentence ('JOHN', 'JOHN', 'VISIT', 'LOVE', 'LOVE') 0 -9.20813720000001 -91.87658903900059
sentence ('JOHN', 'JOHN', 'VISIT', 'LOVE', 'IX') 0 -8.010185899999996 -79.89709247850234
sentence ('JOHN', 'JOHN', 'VISIT', 'MARY', 'MARY') 0 -7.06120940000001 -70.4073285051608
sentence ('JOHN', 'JOHN', 'VISIT', 'MARY', 'LOVE') 0 -6.215045300000014 -61.94569921414557
sentence ('JOHN', 'JOHN', 'VISIT', 'MARY', 'IX') 0 -7.082641600000002 -70.62167865364734
sentence ('JOHN', 'JOHN', 'VISIT', 'ARRIVE', 'MARY') 0 -8.344282300000003 -83.23806467572959
sentence ('JOHN', 'JOHN', 'VISIT', 'ARRIVE', 'LOVE') 0 -9.170051600000008 -91.49576938471436
sentence ('JOHN', 'JOHN', 'VISIT', 'ARRIVE', 'IX') 0 -8.365714499999996 -83.45241482421612
sentence ('JOHN', 'JOHN', 'MARY', 'LOVE', 'MARY') 0 -6.32858929999999 -63.08110550631664
sentence ('JOHN', 'JOHN', 'MARY', 'LOVE', 'LOVE') 0 -8.409267799999995 -83.88790221530142
sentence ('JOHN', 'JOHN', 'MARY', 'LOVE', 'IX') 0 -7.211316499999995 -71.9084056548033
sentence ('JOHN', 'JOHN', 'MARY', 'MARY', 'MARY') 0 -8.16618050000001 -81.45704668146178
sentence ('JOHN', 'JOHN', 'MARY', 'MARY', 'LOVE') 0 -7.3200164 -72.9954173904464
sentence ('JOHN', 'JOHN', 'MARY', 'MARY', 'IX') 0 -8.187612700000003 -81.67139682994832
sentence ('JOHN', 'JOHN', 'MARY', 'ARRIVE', 'MARY') 0 -8.526430699999992 -85.05955585203046
sentence ('JOHN', 'JOHN', 'MARY', 'ARRIVE', 'LOVE') 0 -9.352199999999996 -93.31726056101522
sentence ('JOHN', 'JOHN', 'MARY', 'ARRIVE', 'IX') 0 -8.547862899999984 -85.27390600051699
sentence ('JOHN', 'JOHN', 'GO', 'LOVE', 'MARY') 0 -6.969971299999997 -69.49492652830679
sentence ('JOHN', 'JOHN', 'GO', 'LOVE', 'LOVE') 0 -9.050649800000002 -90.30172323729157
sentence ('JOHN', 'JOHN', 'GO', 'LOVE', 'IX') 0 -7.852698499999988 -78.32222667679332
sentence ('JOHN', 'JOHN', 'GO', 'MARY', 'MARY') 0 -7.826544700000014 -78.0606897034519
sentence ('JOHN', 'JOHN', 'GO', 'MARY', 'LOVE') 0 -6.980380600000004 -69.59906041243653
sentence ('JOHN', 'JOHN', 'GO', 'MARY', 'IX') 0 -7.847976900000006 -78.27503985193843
sentence ('JOHN', 'JOHN', 'GO', 'ARRIVE', 'MARY') 0 -8.186794899999995 -81.66319887402057
sentence ('JOHN', 'JOHN', 'GO', 'ARRIVE', 'LOVE') 0 -9.0125642 -89.92090358300534
sentence ('JOHN', 'JOHN', 'GO', 'ARRIVE', 'IX') 0 -8.208227099999988 -81.8775490225071
Best sentence (-55.31578678452501, ('JOHN', 'IX', 'MARY', 'LOVE', 'MARY'), 0, -5.5520586899999955)
num_sentences 81 0.0961705920053646 seconds
0 candidates ['JOHN', 'MARY']
1 candidates ['JOHN', 'MARY', 'IX']
2 candidates ['BLAME', 'GIVE']
sentence ('JOHN', 'JOHN', 'BLAME') 0 -4.785460399999991 -47.65474152690167
sentence ('JOHN', 'JOHN', 'GIVE') 0 -6.216902599999997 -61.96968179389333
sentence ('JOHN', 'MARY', 'BLAME') 0 -3.9779781000000014 -39.579921639416646
sentence ('JOHN', 'MARY', 'GIVE') 0 -5.2422565000000105 -52.22322390640833
sentence ('JOHN', 'IX', 'BLAME') 0 -4.199498790000007 -41.79513563375363
sentence ('JOHN', 'IX', 'GIVE') 0 -4.454455690000003 -44.345222900745185
sentence ('MARY', 'JOHN', 'BLAME') 0 -5.6535075999999975 -56.33522060890224
sentence ('MARY', 'JOHN', 'GIVE') 0 -7.084949800000004 -70.6501608758939
sentence ('MARY', 'MARY', 'BLAME') 0 -5.789445999999998 -57.694607721417114
sentence ('MARY', 'MARY', 'GIVE') 0 -7.053724400000007 -70.3379099884088
sentence ('MARY', 'IX', 'BLAME') 0 -6.268493100000001 -62.48508581575407
sentence ('MARY', 'IX', 'GIVE') 0 -6.523450000000011 -65.03517308274577
Best sentence (-39.579921639416646, ('JOHN', 'MARY', 'BLAME'), 0, -3.9779781000000014)
num_sentences 12 0.010647559000062756 seconds
0 candidates ['JOHN', 'TEACHER', 'CAR']
1 candidates ['GIVE', 'GIVE', 'GROUP']
2 candidates ['GIVE', 'BLAME', 'JOHN']
3 candidates ['GIVE', 'VISIT', 'IX']
4 candidates ['BLAME', 'WHAT', 'JOHN']
sentence ('JOHN', 'GIVE', 'GIVE', 'GIVE', 'BLAME') 0 -6.626568699999993 -66.05610908004012
sentence ('JOHN', 'GIVE', 'GIVE', 'GIVE', 'WHAT') 0 -6.1555979999999835 -61.34640516032623
sentence ('JOHN', 'GIVE', 'GIVE', 'GIVE', 'JOHN') 0 -7.161976999999993 -71.41019533108557
sentence ('JOHN', 'GIVE', 'GIVE', 'VISIT', 'BLAME') 0 -7.897112799999988 -78.76155033756537
sentence ('JOHN', 'GIVE', 'GIVE', 'VISIT', 'WHAT') 0 -6.841210099999984 -68.20252641785153
sentence ('JOHN', 'GIVE', 'GIVE', 'VISIT', 'JOHN') 0 -8.164010399999981 -81.43052958861075
sentence ('JOHN', 'GIVE', 'GIVE', 'IX', 'BLAME') 0 -6.972405999999992 -69.51448247233499
sentence ('JOHN', 'GIVE', 'GIVE', 'IX', 'WHAT') 0 -6.501435299999997 -64.80477855262124
sentence ('JOHN', 'GIVE', 'GIVE', 'IX', 'JOHN') 0 -7.009136499999997 -69.88179072338049
sentence ('JOHN', 'GIVE', 'BLAME', 'GIVE', 'BLAME') 0 -9.05406520000001 -90.33083579420722
sentence ('JOHN', 'GIVE', 'BLAME', 'GIVE', 'WHAT') 0 -8.583094500000001 -85.62113187449333
sentence ('JOHN', 'GIVE', 'BLAME', 'GIVE', 'JOHN') 0 -9.589473500000011 -95.68492204525268
sentence ('JOHN', 'GIVE', 'BLAME', 'VISIT', 'BLAME') 0 -9.225846600000011 -92.04865005173251
sentence ('JOHN', 'GIVE', 'BLAME', 'VISIT', 'WHAT') 0 -8.169943900000007 -81.48962613201867
sentence ('JOHN', 'GIVE', 'BLAME', 'VISIT', 'JOHN') 0 -9.492744200000004 -94.7176293027779
sentence ('JOHN', 'GIVE', 'BLAME', 'IX', 'BLAME') 0 -8.718031699999997 -86.97050118650196
sentence ('JOHN', 'GIVE', 'BLAME', 'IX', 'WHAT') 0 -8.247060999999988 -82.26079726678807
sentence ('JOHN', 'GIVE', 'BLAME', 'IX', 'JOHN') 0 -8.754762200000002 -87.33780943754746
sentence ('JOHN', 'GIVE', 'JOHN', 'GIVE', 'BLAME') 0 -9.373582400000004 -93.52601375582312
sentence ('JOHN', 'GIVE', 'JOHN', 'GIVE', 'WHAT') 0 -8.902611699999994 -88.81630983610923
sentence ('JOHN', 'GIVE', 'JOHN', 'GIVE', 'JOHN') 0 -9.908990700000004 -98.88010000686857
sentence ('JOHN', 'GIVE', 'JOHN', 'VISIT', 'BLAME') 0 -8.399758599999998 -83.78777601334836
sentence ('JOHN', 'GIVE', 'JOHN', 'VISIT', 'WHAT') 0 -7.343855899999994 -73.22875209363453
sentence ('JOHN', 'GIVE', 'JOHN', 'VISIT', 'JOHN') 0 -8.666656199999991 -86.45675526439375
sentence ('JOHN', 'GIVE', 'JOHN', 'IX', 'BLAME') 0 -8.294132289999993 -82.73151304811789
sentence ('JOHN', 'GIVE', 'JOHN', 'IX', 'WHAT') 0 -7.823161589999998 -78.02180912840414
sentence ('JOHN', 'GIVE', 'JOHN', 'IX', 'JOHN') 0 -8.330862789999998 -83.09882129916339
sentence ('JOHN', 'GIVE', 'GIVE', 'GIVE', 'BLAME') 0 -6.626568699999993 -66.05610908004012
sentence ('JOHN', 'GIVE', 'GIVE', 'GIVE', 'WHAT') 0 -6.1555979999999835 -61.34640516032623
sentence ('JOHN', 'GIVE', 'GIVE', 'GIVE', 'JOHN') 0 -7.161976999999993 -71.41019533108557
sentence ('JOHN', 'GIVE', 'GIVE', 'VISIT', 'BLAME') 0 -7.897112799999988 -78.76155033756537
sentence ('JOHN', 'GIVE', 'GIVE', 'VISIT', 'WHAT') 0 -6.841210099999984 -68.20252641785153
sentence ('JOHN', 'GIVE', 'GIVE', 'VISIT', 'JOHN') 0 -8.164010399999981 -81.43052958861075
sentence ('JOHN', 'GIVE', 'GIVE', 'IX', 'BLAME') 0 -6.972405999999992 -69.51448247233499
sentence ('JOHN', 'GIVE', 'GIVE', 'IX', 'WHAT') 0 -6.501435299999997 -64.80477855262124
sentence ('JOHN', 'GIVE', 'GIVE', 'IX', 'JOHN') 0 -7.009136499999997 -69.88179072338049
sentence ('JOHN', 'GIVE', 'BLAME', 'GIVE', 'BLAME') 0 -9.05406520000001 -90.33083579420722
sentence ('JOHN', 'GIVE', 'BLAME', 'GIVE', 'WHAT') 0 -8.583094500000001 -85.62113187449333
sentence ('JOHN', 'GIVE', 'BLAME', 'GIVE', 'JOHN') 0 -9.589473500000011 -95.68492204525268
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sentence ('CAR', 'GROUP', 'JOHN', 'IX', 'JOHN') 0 -11.288204189999988 -112.6720189830173
Best sentence (-61.214135237141662, ('TEACHER', 'GIVE', 'GIVE', 'GIVE', 'WHAT'), 0, -6.142370999999983)
num_sentences 243 0.3191005970002152 seconds
0 candidates ['JOHN', 'GO', 'ARRIVE']
1 candidates ['BOX', 'JOHN', 'BLAME']
sentence ('JOHN', 'BOX') 0 -2.7946396399999998 -27.73403834562475
sentence ('JOHN', 'JOHN') 0 -3.6551369999999963 -36.33905642084759
sentence ('JOHN', 'BLAME') 0 -3.057979700000004 -30.367484268285672
sentence ('GO', 'BOX') 0 -5.362167900000003 -53.409406807659096
sentence ('GO', 'JOHN') 0 -5.579544900000002 -55.583221282881965
sentence ('GO', 'BLAME') 0 -5.312647300000009 -52.91424613032003
sentence ('ARRIVE', 'BOX') 0 -5.492025600000005 -54.70800323233675
sentence ('ARRIVE', 'JOHN') 0 -5.709402600000004 -56.88181770755962
sentence ('ARRIVE', 'BLAME') 0 -5.442504999999997 -54.21284255499754
Best sentence (-27.73403834562475, ('JOHN', 'BOX'), 0, -2.7946396399999998)
num_sentences 9 0.0061729130102321506 seconds
0 candidates ['IX', 'JOHN', 'GO']
1 candidates ['BOY', 'IX', 'GIVE']
2 candidates ['GIVE', 'STUDENT', 'TEACHER']
3 candidates ['TEACHER', 'HOUSE', 'GIVE']
4 candidates ['APPLE']
sentence ('IX', 'BOY', 'GIVE', 'TEACHER', 'APPLE') 0 -5.1418496999999945 -51.21348101331661
sentence ('IX', 'BOY', 'GIVE', 'HOUSE', 'APPLE') 0 -10.920874400000002 -109.00373331437805
sentence ('IX', 'BOY', 'GIVE', 'GIVE', 'APPLE') 0 -9.415455399999999 -93.9497781708713
sentence ('IX', 'BOY', 'STUDENT', 'TEACHER', 'APPLE') 0 -9.87313589999998 -98.5261531442843
sentence ('IX', 'BOY', 'STUDENT', 'HOUSE', 'APPLE') 0 -11.989124899999993 -119.68604844534579
sentence ('IX', 'BOY', 'STUDENT', 'GIVE', 'APPLE') 0 -11.582468599999999 -115.61972030183912
sentence ('IX', 'BOY', 'TEACHER', 'TEACHER', 'APPLE') 0 -9.697044599999984 -96.76524014814429
sentence ('IX', 'BOY', 'TEACHER', 'HOUSE', 'APPLE') 0 -11.813033599999997 -117.92513544920578
sentence ('IX', 'BOY', 'TEACHER', 'GIVE', 'APPLE') 0 -10.366506399999992 -103.460098305699
sentence ('IX', 'IX', 'GIVE', 'TEACHER', 'APPLE') 0 -6.875764600000011 -68.55263560609605
sentence ('IX', 'IX', 'GIVE', 'HOUSE', 'APPLE') 0 -10.746573400000003 -107.26072890715731
sentence ('IX', 'IX', 'GIVE', 'GIVE', 'APPLE') 0 -9.2411544 -92.20677376365056
sentence ('IX', 'IX', 'STUDENT', 'TEACHER', 'APPLE') 0 -9.524785099999988 -95.04265073706365
sentence ('IX', 'IX', 'STUDENT', 'HOUSE', 'APPLE') 0 -11.640774100000002 -116.20254603812515
sentence ('IX', 'IX', 'STUDENT', 'GIVE', 'APPLE') 0 -11.234117800000007 -112.13621789461848
sentence ('IX', 'IX', 'TEACHER', 'TEACHER', 'APPLE') 0 -9.348693799999992 -93.28173774092365
sentence ('IX', 'IX', 'TEACHER', 'HOUSE', 'APPLE') 0 -11.464682800000006 -114.44163304198514
sentence ('IX', 'IX', 'TEACHER', 'GIVE', 'APPLE') 0 -10.0181556 -99.97659589847837
sentence ('IX', 'GIVE', 'GIVE', 'TEACHER', 'APPLE') 0 -5.718696399999999 -56.981960888143064
sentence ('IX', 'GIVE', 'GIVE', 'HOUSE', 'APPLE') 0 -9.58950519999999 -95.69005418920435
sentence ('IX', 'GIVE', 'GIVE', 'GIVE', 'APPLE') 0 -8.084086199999987 -80.63609904569759
sentence ('IX', 'GIVE', 'STUDENT', 'TEACHER', 'APPLE') 0 -9.155026499999977 -91.34507201911067
sentence ('IX', 'GIVE', 'STUDENT', 'HOUSE', 'APPLE') 0 -11.27101549999999 -112.50496732017217
sentence ('IX', 'GIVE', 'STUDENT', 'GIVE', 'APPLE') 0 -10.864359199999996 -108.4386391766655
sentence ('IX', 'GIVE', 'TEACHER', 'TEACHER', 'APPLE') 0 -8.439601599999989 -84.19082302297076
sentence ('IX', 'GIVE', 'TEACHER', 'HOUSE', 'APPLE') 0 -10.555590600000002 -105.35071832403224
sentence ('IX', 'GIVE', 'TEACHER', 'GIVE', 'APPLE') 0 -9.109063399999997 -90.88568118052547
sentence ('JOHN', 'BOY', 'GIVE', 'TEACHER', 'APPLE') 0 -4.385179600000001 -43.64681405395367
sentence ('JOHN', 'BOY', 'GIVE', 'HOUSE', 'APPLE') 0 -10.164204300000009 -101.43706635501512
sentence ('JOHN', 'BOY', 'GIVE', 'GIVE', 'APPLE') 0 -8.658785300000005 -86.38311121150835
sentence ('JOHN', 'BOY', 'STUDENT', 'TEACHER', 'APPLE') 0 -9.116465799999986 -90.95948618492136
sentence ('JOHN', 'BOY', 'STUDENT', 'HOUSE', 'APPLE') 0 -11.2324548 -112.11938148598286
sentence ('JOHN', 'BOY', 'STUDENT', 'GIVE', 'APPLE') 0 -10.825798500000005 -108.05305334247619
sentence ('JOHN', 'BOY', 'TEACHER', 'TEACHER', 'APPLE') 0 -8.94037449999999 -89.19857318878135
sentence ('JOHN', 'BOY', 'TEACHER', 'HOUSE', 'APPLE') 0 -11.056363500000003 -110.35846848984285
sentence ('JOHN', 'BOY', 'TEACHER', 'GIVE', 'APPLE') 0 -9.609836299999998 -95.89343134633607
sentence ('JOHN', 'IX', 'GIVE', 'TEACHER', 'APPLE') 0 -4.89077279 -48.702751546732934
sentence ('JOHN', 'IX', 'GIVE', 'HOUSE', 'APPLE') 0 -8.761581589999992 -87.41084484779421
sentence ('JOHN', 'IX', 'GIVE', 'GIVE', 'APPLE') 0 -7.256162589999988 -72.35688970428745
sentence ('JOHN', 'IX', 'STUDENT', 'TEACHER', 'APPLE') 0 -7.539793289999977 -75.19276667770055
sentence ('JOHN', 'IX', 'STUDENT', 'HOUSE', 'APPLE') 0 -9.65578228999999 -96.35266197876204
sentence ('JOHN', 'IX', 'STUDENT', 'GIVE', 'APPLE') 0 -9.249125989999996 -92.28633383525536
sentence ('JOHN', 'IX', 'TEACHER', 'TEACHER', 'APPLE') 0 -7.363701989999981 -73.43185368156054
sentence ('JOHN', 'IX', 'TEACHER', 'HOUSE', 'APPLE') 0 -9.479690989999995 -94.59174898262204
sentence ('JOHN', 'IX', 'TEACHER', 'GIVE', 'APPLE') 0 -8.033163789999989 -80.12671183911526
sentence ('JOHN', 'GIVE', 'GIVE', 'TEACHER', 'APPLE') 0 -4.765507299999996 -47.45010392878004
sentence ('JOHN', 'GIVE', 'GIVE', 'HOUSE', 'APPLE') 0 -8.636316099999988 -86.15819722984132
sentence ('JOHN', 'GIVE', 'GIVE', 'GIVE', 'APPLE') 0 -7.130897099999984 -71.10424208633457
sentence ('JOHN', 'GIVE', 'STUDENT', 'TEACHER', 'APPLE') 0 -8.201837399999988 -81.81321505974779
sentence ('JOHN', 'GIVE', 'STUDENT', 'HOUSE', 'APPLE') 0 -10.317826400000001 -102.97311036080929
sentence ('JOHN', 'GIVE', 'STUDENT', 'GIVE', 'APPLE') 0 -9.911170100000007 -98.90678221730262
sentence ('JOHN', 'GIVE', 'TEACHER', 'TEACHER', 'APPLE') 0 -7.486412499999986 -74.65896606360772
sentence ('JOHN', 'GIVE', 'TEACHER', 'HOUSE', 'APPLE') 0 -9.6024015 -95.81886136466922
sentence ('JOHN', 'GIVE', 'TEACHER', 'GIVE', 'APPLE') 0 -8.155874299999994 -81.35382422116244
sentence ('GO', 'BOY', 'GIVE', 'TEACHER', 'APPLE') 0 -6.309587499999992 -62.89089336943355
sentence ('GO', 'BOY', 'GIVE', 'HOUSE', 'APPLE') 0 -12.0886122 -120.681145670495
sentence ('GO', 'BOY', 'GIVE', 'GIVE', 'APPLE') 0 -10.583193199999997 -105.62719052698824
sentence ('GO', 'BOY', 'STUDENT', 'TEACHER', 'APPLE') 0 -11.040873699999977 -110.20356550040124
sentence ('GO', 'BOY', 'STUDENT', 'HOUSE', 'APPLE') 0 -13.15686269999999 -131.36346080146274
sentence ('GO', 'BOY', 'STUDENT', 'GIVE', 'APPLE') 0 -12.750206399999996 -127.29713265795607
sentence ('GO', 'BOY', 'TEACHER', 'TEACHER', 'APPLE') 0 -10.864782399999982 -108.44265250426123
sentence ('GO', 'BOY', 'TEACHER', 'HOUSE', 'APPLE') 0 -12.980771399999995 -129.60254780532273
sentence ('GO', 'BOY', 'TEACHER', 'GIVE', 'APPLE') 0 -11.53424419999999 -115.13751066181595
sentence ('GO', 'IX', 'GIVE', 'TEACHER', 'APPLE') 0 -7.848563500000012 -78.28065896221301
sentence ('GO', 'IX', 'GIVE', 'HOUSE', 'APPLE') 0 -11.719372300000003 -116.98875226327429
sentence ('GO', 'IX', 'GIVE', 'GIVE', 'APPLE') 0 -10.2139533 -101.93479711976754
sentence ('GO', 'IX', 'STUDENT', 'TEACHER', 'APPLE') 0 -10.49758399999999 -104.77067409318063
sentence ('GO', 'IX', 'STUDENT', 'HOUSE', 'APPLE') 0 -12.613573000000002 -125.93056939424213
sentence ('GO', 'IX', 'STUDENT', 'GIVE', 'APPLE') 0 -12.206916700000008 -121.86424125073546
sentence ('GO', 'IX', 'TEACHER', 'TEACHER', 'APPLE') 0 -10.321492699999993 -103.00976109704062
sentence ('GO', 'IX', 'TEACHER', 'HOUSE', 'APPLE') 0 -12.437481700000006 -124.16965639810212
sentence ('GO', 'IX', 'TEACHER', 'GIVE', 'APPLE') 0 -10.9909545 -109.70461925459534
sentence ('GO', 'GIVE', 'GIVE', 'TEACHER', 'APPLE') 0 -7.197887300000005 -71.7739042442601
sentence ('GO', 'GIVE', 'GIVE', 'HOUSE', 'APPLE') 0 -11.068696099999997 -110.48199754532138
sentence ('GO', 'GIVE', 'GIVE', 'GIVE', 'APPLE') 0 -9.563277099999993 -95.42804240181462
sentence ('GO', 'GIVE', 'STUDENT', 'TEACHER', 'APPLE') 0 -10.634217399999983 -106.1370153752277
sentence ('GO', 'GIVE', 'STUDENT', 'HOUSE', 'APPLE') 0 -12.750206399999996 -127.2969106762892
sentence ('GO', 'GIVE', 'STUDENT', 'GIVE', 'APPLE') 0 -12.343550100000002 -123.23058253278253
sentence ('GO', 'GIVE', 'TEACHER', 'TEACHER', 'APPLE') 0 -9.918792499999995 -98.98276637908779
sentence ('GO', 'GIVE', 'TEACHER', 'HOUSE', 'APPLE') 0 -12.034781500000008 -120.14266168014927
sentence ('GO', 'GIVE', 'TEACHER', 'GIVE', 'APPLE') 0 -10.588254300000003 -105.6776245366425
Best sentence (-43.646814053953669, ('JOHN', 'BOY', 'GIVE', 'TEACHER', 'APPLE'), 0, -4.385179600000001)
num_sentences 81 0.09941759800130967 seconds
0 candidates []
1 candidates ['IX', 'VISIT', 'JOHN']
2 candidates ['GIVE', 'VISIT', 'GIVE']
3 candidates ['BOX', 'ARRIVE', 'CAN']
sentence ('MARY', 'IX', 'GIVE', 'BOX') 0 -7.987836200000004 -79.69254643466564
sentence ('MARY', 'IX', 'GIVE', 'ARRIVE') 0 -7.606073100000003 -75.87493175527534
sentence ('MARY', 'IX', 'GIVE', 'CAN') 0 -8.070438300000006 -80.51859243686178
sentence ('MARY', 'IX', 'VISIT', 'BOX') 0 -8.471070700000013 -84.5248940604485
sentence ('MARY', 'IX', 'VISIT', 'ARRIVE') 0 -8.089307599999998 -80.70727938105807
sentence ('MARY', 'IX', 'VISIT', 'CAN') 0 -8.553672800000001 -85.35094006264451
sentence ('MARY', 'IX', 'GIVE', 'BOX') 0 -7.987836200000004 -79.69254643466564
sentence ('MARY', 'IX', 'GIVE', 'ARRIVE') 0 -7.606073100000003 -75.87493175527534
sentence ('MARY', 'IX', 'GIVE', 'CAN') 0 -8.070438300000006 -80.51859243686178
sentence ('MARY', 'VISIT', 'GIVE', 'BOX') 0 -8.24397780000001 -82.25396450468132
sentence ('MARY', 'VISIT', 'GIVE', 'ARRIVE') 0 -7.86221470000001 -78.43634982529102
sentence ('MARY', 'VISIT', 'GIVE', 'CAN') 0 -8.326579900000013 -83.08001050687746
sentence ('MARY', 'VISIT', 'VISIT', 'BOX') 0 -8.415759200000025 -83.97178113046425
sentence ('MARY', 'VISIT', 'VISIT', 'ARRIVE') 0 -8.03399610000001 -80.1541664510738
sentence ('MARY', 'VISIT', 'VISIT', 'CAN') 0 -8.498361300000013 -84.79782713266025
sentence ('MARY', 'VISIT', 'GIVE', 'BOX') 0 -8.24397780000001 -82.25396450468132
sentence ('MARY', 'VISIT', 'GIVE', 'ARRIVE') 0 -7.86221470000001 -78.43634982529102
sentence ('MARY', 'VISIT', 'GIVE', 'CAN') 0 -8.326579900000013 -83.08001050687746
sentence ('MARY', 'JOHN', 'GIVE', 'BOX') 0 -8.549336000000011 -85.30754706982788
sentence ('MARY', 'JOHN', 'GIVE', 'ARRIVE') 0 -8.167572899999996 -81.48993239043743
sentence ('MARY', 'JOHN', 'GIVE', 'CAN') 0 -8.6319381 -86.13359307202387
sentence ('MARY', 'JOHN', 'VISIT', 'BOX') 0 -7.57551220000002 -75.56931169561074
sentence ('MARY', 'JOHN', 'VISIT', 'ARRIVE') 0 -7.193749100000005 -71.7516970162203
sentence ('MARY', 'JOHN', 'VISIT', 'CAN') 0 -7.658114300000008 -76.39535769780674
sentence ('MARY', 'JOHN', 'GIVE', 'BOX') 0 -8.549336000000011 -85.30754706982788
sentence ('MARY', 'JOHN', 'GIVE', 'ARRIVE') 0 -8.167572899999996 -81.48993239043743
sentence ('MARY', 'JOHN', 'GIVE', 'CAN') 0 -8.6319381 -86.13359307202387
Best sentence (-71.751697016220305, ('MARY', 'JOHN', 'VISIT', 'ARRIVE'), 0, -7.193749100000005)
num_sentences 27 0.027623139991192147 seconds
0 candidates ['JOHN', 'MARY']
1 candidates ['JOHN', 'IX', 'POSS']
2 candidates ['GIVE', 'VISIT', 'GO']
3 candidates ['BOX']
sentence ('JOHN', 'JOHN', 'GIVE', 'BOX') 0 -7.681288800000004 -76.60871294978035
sentence ('JOHN', 'JOHN', 'VISIT', 'BOX') 0 -6.707465000000013 -66.87048446013209
sentence ('JOHN', 'JOHN', 'GO', 'BOX') 0 -6.549977600000005 -65.29561660646867
sentence ('JOHN', 'IX', 'GIVE', 'BOX') 0 -5.918841889999996 -58.98424634982544
sentence ('JOHN', 'IX', 'VISIT', 'BOX') 0 -6.402076390000005 -63.816600860177175
sentence ('JOHN', 'IX', 'GO', 'BOX') 0 -6.296290290000002 -62.75874600651381
sentence ('JOHN', 'POSS', 'GIVE', 'BOX') 0 -6.281177 -62.60759857733696
sentence ('JOHN', 'POSS', 'VISIT', 'BOX') 0 -6.452958400000014 -64.32542208768874
sentence ('JOHN', 'POSS', 'GO', 'BOX') 0 -6.347172299999997 -63.267567234025236
sentence ('MARY', 'JOHN', 'GIVE', 'BOX') 0 -8.549336000000011 -85.28918882526314
sentence ('MARY', 'JOHN', 'VISIT', 'BOX') 0 -7.57551220000002 -75.55096033561487
sentence ('MARY', 'JOHN', 'GO', 'BOX') 0 -7.418024800000012 -73.97609248195145
sentence ('MARY', 'IX', 'GIVE', 'BOX') 0 -7.987836200000004 -79.67419332530825
sentence ('MARY', 'IX', 'VISIT', 'BOX') 0 -8.471070700000013 -84.50654783565997
sentence ('MARY', 'IX', 'GO', 'BOX') 0 -8.365284599999995 -83.44869298199647
sentence ('MARY', 'POSS', 'GIVE', 'BOX') 0 -8.689004900000015 -86.68588145281983
sentence ('MARY', 'POSS', 'VISIT', 'BOX') 0 -8.86078630000003 -88.40370496317162
sentence ('MARY', 'POSS', 'GO', 'BOX') 0 -8.755000200000012 -87.3458501095081
Best sentence (-58.98424634982544, ('JOHN', 'IX', 'GIVE', 'BOX'), 0, -5.918841889999996)
num_sentences 18 0.019243085000198334 seconds
0 candidates ['JOHN', 'MARY', 'IX']
1 candidates ['CHOCOLATE', 'ARRIVE', 'STUDENT']
2 candidates ['MARY', 'WHO', 'JOHN']
sentence ('JOHN', 'CHOCOLATE', 'MARY') 0 -5.540414600000005 -55.226978592639156
sentence ('JOHN', 'CHOCOLATE', 'WHO') 0 -4.809750499999993 -47.92034511426204
sentence ('JOHN', 'CHOCOLATE', 'JOHN') 0 -6.345287900000002 -63.2757214347398
sentence ('JOHN', 'ARRIVE', 'MARY') 0 -4.347690200000002 -43.29973462112163
sentence ('JOHN', 'ARRIVE', 'WHO') 0 -3.0676579000000004 -30.499419142744625
sentence ('JOHN', 'ARRIVE', 'JOHN') 0 -5.152563499999999 -51.348477463222274
sentence ('JOHN', 'STUDENT', 'MARY') 0 -5.414286900000008 -53.96570993004499
sentence ('JOHN', 'STUDENT', 'WHO') 0 -5.599729400000001 -55.820142451667934
sentence ('JOHN', 'STUDENT', 'JOHN') 0 -6.219160200000005 -62.01445277214563
sentence ('MARY', 'CHOCOLATE', 'MARY') 0 -6.7754262000000125 -67.57710495516133
sentence ('MARY', 'CHOCOLATE', 'WHO') 0 -6.0447621 -60.270471476784216
sentence ('MARY', 'CHOCOLATE', 'JOHN') 0 -7.5802995000000095 -75.62584779726197
sentence ('MARY', 'ARRIVE', 'MARY') 0 -6.3095889000000085 -62.918731983643795
sentence ('MARY', 'ARRIVE', 'WHO') 0 -5.0295566000000065 -50.11841650526679
sentence ('MARY', 'ARRIVE', 'JOHN') 0 -7.1144622000000055 -70.96747482574445
sentence ('MARY', 'STUDENT', 'MARY') 0 -6.649298500000015 -66.31583629256716
sentence ('MARY', 'STUDENT', 'WHO') 0 -6.834741000000008 -68.17026881419011
sentence ('MARY', 'STUDENT', 'JOHN') 0 -7.454171800000012 -74.3645791346678
sentence ('IX', 'CHOCOLATE', 'MARY') 0 -6.297084699999999 -62.79370532868128
sentence ('IX', 'CHOCOLATE', 'WHO') 0 -5.5664205999999865 -55.48707185030416
sentence ('IX', 'CHOCOLATE', 'JOHN') 0 -7.101957999999996 -70.84244817078192
sentence ('IX', 'ARRIVE', 'MARY') 0 -5.831247399999995 -58.13533235716374
sentence ('IX', 'ARRIVE', 'WHO') 0 -4.551215099999993 -45.33501687878673
sentence ('IX', 'ARRIVE', 'JOHN') 0 -6.636120699999992 -66.18407519926438
sentence ('IX', 'STUDENT', 'MARY') 0 -6.170957000000001 -61.53243666608711
sentence ('IX', 'STUDENT', 'WHO') 0 -6.356399499999995 -63.386869187710055
sentence ('IX', 'STUDENT', 'JOHN') 0 -6.975830299999998 -69.58117950818774
Best sentence (-30.499419142744625, ('JOHN', 'ARRIVE', 'WHO'), 0, -3.0676579000000004)
num_sentences 27 0.020824737002840266 seconds
0 candidates ['JOHN']
1 candidates ['MARY', 'SHOULD', 'GIVE']
2 candidates ['WOMAN', 'LOVE']
3 candidates ['WOMAN', 'JOHN', 'IX']
4 candidates ['BUY', 'NEW', 'JOHN']
5 candidates ['HOUSE', 'TEACHER', 'CAR']
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'BUY', 'HOUSE') 0 -8.783436629999997 -87.60892921811558
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'BUY', 'TEACHER') 0 -12.086148600000001 -120.63605349197806
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'BUY', 'CAR') 0 -10.6025573 -105.80014300690335
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'NEW', 'HOUSE') 0 -11.898491199999995 -118.75947698838928
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'NEW', 'TEACHER') 0 -12.367229099999989 -123.44686056225164
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'NEW', 'CAR') 0 -10.884989099999999 -108.62446307717705
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'JOHN', 'HOUSE') 0 -11.238634000000005 -112.16090677462844
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'JOHN', 'TEACHER') 0 -11.707371899999998 -116.84829034849082
sentence ('JOHN', 'MARY', 'WOMAN', 'WOMAN', 'JOHN', 'CAR') 0 -11.234469200000007 -112.1192658634162
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'BUY', 'HOUSE') 0 -8.858241730000003 -88.35698607073553
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'BUY', 'TEACHER') 0 -12.160953700000007 -121.38411034459801
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'BUY', 'CAR') 0 -9.443189100000012 -94.20646685952337
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'NEW', 'HOUSE') 0 -11.4604827 -114.37939784100922
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'NEW', 'TEACHER') 0 -11.929220599999994 -119.06678141487158
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'NEW', 'CAR') 0 -10.446980600000003 -104.244383929797
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'JOHN', 'HOUSE') 0 -10.80062550000001 -107.78082762724839
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'JOHN', 'TEACHER') 0 -11.269363400000003 -112.46821120111076
sentence ('JOHN', 'MARY', 'WOMAN', 'JOHN', 'JOHN', 'CAR') 0 -10.796460700000011 -107.73918671603614
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'BUY', 'HOUSE') 0 -7.820668330000004 -77.98125234126627
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'BUY', 'TEACHER') 0 -11.123380300000008 -111.00837661512874
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'BUY', 'CAR') 0 -9.639789000000007 -96.17246613005405
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'NEW', 'HOUSE') 0 -10.444557799999998 -104.22014911153993
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'NEW', 'TEACHER') 0 -10.913295699999992 -108.9075326854023
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'NEW', 'CAR') 0 -9.431055700000002 -94.08513520032771
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'JOHN', 'HOUSE') 0 -10.045698600000009 -100.23155889777911
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'JOHN', 'TEACHER') 0 -10.514436500000002 -104.91894247164147
sentence ('JOHN', 'MARY', 'WOMAN', 'IX', 'JOHN', 'CAR') 0 -10.04153380000001 -100.18991798656687
sentence ('JOHN', 'MARY', 'LOVE', 'WOMAN', 'BUY', 'HOUSE') 0 -7.899328830000002 -78.76788798377899
sentence ('JOHN', 'MARY', 'LOVE', 'WOMAN', 'BUY', 'TEACHER') 0 -11.202040800000006 -111.79501225764147
sentence ('JOHN', 'MARY', 'LOVE', 'WOMAN', 'BUY', 'CAR') 0 -9.718449500000006 -96.95910177256678
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sentence ('JOHN', 'SHOULD', 'LOVE', 'JOHN', 'JOHN', 'TEACHER') 0 -11.0248141 -110.02275938418646
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sentence ('JOHN', 'SHOULD', 'LOVE', 'IX', 'BUY', 'HOUSE') 0 -7.577591229999996 -75.55052252434191
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sentence ('JOHN', 'SHOULD', 'LOVE', 'IX', 'JOHN', 'HOUSE') 0 -9.8026215 -97.80082908085475
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sentence ('JOHN', 'GIVE', 'WOMAN', 'WOMAN', 'JOHN', 'CAR') 0 -11.420165600000004 -113.976368662896
sentence ('JOHN', 'GIVE', 'WOMAN', 'JOHN', 'BUY', 'HOUSE') 0 -9.04393813 -90.2140888702153
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sentence ('JOHN', 'GIVE', 'WOMAN', 'JOHN', 'NEW', 'TEACHER') 0 -12.114917000000005 -120.92388421435152
sentence ('JOHN', 'GIVE', 'WOMAN', 'JOHN', 'NEW', 'CAR') 0 -10.632677000000015 -106.10148672927691
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sentence ('JOHN', 'GIVE', 'WOMAN', 'IX', 'JOHN', 'TEACHER') 0 -10.7001329 -106.77604527112126
sentence ('JOHN', 'GIVE', 'WOMAN', 'IX', 'JOHN', 'CAR') 0 -10.227230200000008 -102.04702078604666
sentence ('JOHN', 'GIVE', 'LOVE', 'WOMAN', 'BUY', 'HOUSE') 0 -9.066043030000003 -90.4351687832588
sentence ('JOHN', 'GIVE', 'LOVE', 'WOMAN', 'BUY', 'TEACHER') 0 -12.368755000000007 -123.46229305712129
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sentence ('JOHN', 'GIVE', 'LOVE', 'WOMAN', 'NEW', 'HOUSE') 0 -12.181097600000001 -121.5857165535325
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sentence ('JOHN', 'GIVE', 'LOVE', 'WOMAN', 'NEW', 'CAR') 0 -11.167595500000004 -111.4507026423203
sentence ('JOHN', 'GIVE', 'LOVE', 'WOMAN', 'JOHN', 'HOUSE') 0 -11.52124040000001 -114.98714633977167
sentence ('JOHN', 'GIVE', 'LOVE', 'WOMAN', 'JOHN', 'TEACHER') 0 -11.989978300000004 -119.67452991363405
sentence ('JOHN', 'GIVE', 'LOVE', 'WOMAN', 'JOHN', 'CAR') 0 -11.517075600000013 -114.94550542855944
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'BUY', 'HOUSE') 0 -9.18828293 -91.65757363587868
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'BUY', 'TEACHER') 0 -12.490994900000004 -124.68469790974115
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'BUY', 'CAR') 0 -9.773230300000009 -97.5070544246665
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'NEW', 'HOUSE') 0 -11.79052390000001 -117.6799854061525
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'NEW', 'TEACHER') 0 -12.259261800000004 -122.36736898001487
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'NEW', 'CAR') 0 -10.777021800000014 -107.54497149494028
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'JOHN', 'HOUSE') 0 -11.130666700000006 -111.08141519239152
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'JOHN', 'TEACHER') 0 -11.5994046 -115.7687987662539
sentence ('JOHN', 'GIVE', 'LOVE', 'JOHN', 'JOHN', 'CAR') 0 -11.126501900000008 -111.03977428117929
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'BUY', 'HOUSE') 0 -8.152181729999995 -81.29656190640935
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'BUY', 'TEACHER') 0 -11.4548937 -114.32368618027184
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'BUY', 'CAR') 0 -9.971302399999999 -99.48777569519714
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'NEW', 'HOUSE') 0 -10.77607119999999 -107.53545867668302
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'NEW', 'TEACHER') 0 -11.244809099999983 -112.2228422505454
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'NEW', 'CAR') 0 -9.762569099999993 -97.4004447654708
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'JOHN', 'HOUSE') 0 -10.377212 -103.54686846292219
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'JOHN', 'TEACHER') 0 -10.845949899999994 -108.23425203678457
sentence ('JOHN', 'GIVE', 'LOVE', 'IX', 'JOHN', 'CAR') 0 -10.373047200000002 -103.50522755170996
Best sentence (-69.629281106929682, ('JOHN', 'MARY', 'LOVE', 'IX', 'BUY', 'HOUSE'), 0, -6.985467530000008)
num_sentences 162 0.2640409890009323 seconds
In [113]:
show_errors(new_guesses, test_set)
**** WER = 0.4887640449438202
Total correct: 91 out of 178
Video Recognized Correct
=====================================================================================================
2: JOHN *BROTHER *ARRIVE JOHN WRITE HOMEWORK
7: JOHN *PEOPLE GO CAN JOHN CAN GO CAN
12: JOHN *WHAT GO CAN JOHN CAN GO CAN
21: JOHN *ARRIVE *JOHN *MARY *NEW *CAR *FUTURE *MARY JOHN FISH WONT EAT BUT CAN EAT CHICKEN
25: JOHN *IX IX IX IX JOHN LIKE IX IX IX
28: JOHN *IX IX IX IX JOHN LIKE IX IX IX
30: JOHN LIKE IX IX IX JOHN LIKE IX IX IX
36: *JOHN *MARY *IX IX *IX *IX MARY VEGETABLE KNOW IX LIKE CORN1
40: JOHN IX *CORN MARY LOVE JOHN IX THINK MARY LOVE
43: JOHN *FUTURE BUY HOUSE JOHN MUST BUY HOUSE
50: *JOHN *SEE BUY CAR *IX FUTURE JOHN BUY CAR SHOULD
54: JOHN SHOULD NOT BUY HOUSE JOHN SHOULD NOT BUY HOUSE
57: JOHN *IX *IX *IX JOHN DECIDE VISIT MARY
67: JOHN *IX *MARY BUY HOUSE JOHN FUTURE NOT BUY HOUSE
71: JOHN *FUTURE *JOHN MARY JOHN WILL VISIT MARY
74: JOHN *GO *IX *IX JOHN NOT VISIT MARY
77: *JOHN *MARY *LOVE ANN BLAME MARY
84: *JOHN *BUY *CAR BOOK IX-1P FIND SOMETHING-ONE BOOK
89: *MARY IX *IX *IX IX NEW COAT JOHN IX GIVE MAN IX NEW COAT
90: JOHN *IX IX *IX *IX BOOK JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
92: JOHN *IX IX *IX *IX BOOK JOHN GIVE IX SOMETHING-ONE WOMAN BOOK
100: POSS NEW CAR BREAK-DOWN POSS NEW CAR BREAK-DOWN
105: JOHN *SEE JOHN LEG
107: JOHN *IX *JOHN *GO *MARY JOHN POSS FRIEND HAVE CANDY
108: *MARY *LOVE WOMAN ARRIVE
113: *JOHN *ARRIVE *IX *IX *IX IX CAR BLUE SUE BUY
119: *MARY *LOVE IX *JOHN *IX SUE BUY IX CAR BLUE
122: *SOMETHING-ONE *STUDENT BOOK JOHN READ BOOK
139: JOHN BUY *CAR *WHAT BOOK JOHN BUY WHAT YESTERDAY BOOK
142: JOHN BUY YESTERDAY WHAT BOOK JOHN BUY YESTERDAY WHAT BOOK
158: LOVE JOHN WHO LOVE JOHN WHO
167: JOHN IX *MARY LOVE MARY JOHN IX SAY LOVE MARY
171: JOHN MARY BLAME JOHN MARY BLAME
174: *TEACHER *GIVE *GIVE *GIVE *WHAT PEOPLE GROUP GIVE1 JANA TOY
181: JOHN *BOX JOHN ARRIVE
184: *JOHN BOY GIVE TEACHER APPLE ALL BOY GIVE TEACHER APPLE
189: *MARY *JOHN *VISIT *ARRIVE JOHN GIVE GIRL BOX
193: JOHN *IX *GIVE BOX JOHN GIVE GIRL BOX
199: *JOHN *ARRIVE WHO LIKE CHOCOLATE WHO
201: JOHN *MARY *LOVE *IX BUY HOUSE JOHN TELL MARY IX-1P BUY HOUSE
I attempted to integrate the results of the 3gram ARPA model with the existing HMM model however the results were lacklustre.
I believe that either my original models are have been overfit and or I do not have the correct way to combine the results from the ARPA LM with the existing results. I tried several different methods including, multiplying, scoring the different ngrams of the sentence and even tried the bigram model as well.
One problem is generating the product of the sentence possibilities gets really large with some of the sentences like the one with 8 words in it. For the moment I have limited the number of candidate words per sentence position to 3, but a more optimal solution would attempt to allow more but restrict them once a certain number was reached, or used an alternative heuristic to determine which ones to pick.
Unfortunately I didn't have more time to explore possible solutions or improvements but I learnt a lot and it was fun to see the NLP results along side the HMM results.
In [ ]:
Content source: madhavajay/nd889
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