``````

In [1]:

# HMM smoother

``````
``````

In [16]:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import math
import os
from hmmlearn import hmm

from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from sklearn.preprocessing import PolynomialFeatures

from collections import deque
from itertools import islice

from wrangle_updated import trial
from utilities import convert_to_words, get_position_stats, combine_csv, resolve_acc_gyro, blank_filter, concat_data

TIME_SEQUENCE_LENGTH = 50
DIR = os.path.dirname(os.path.realpath('__file__'))
polynomial_features = PolynomialFeatures(interaction_only=False, include_bias=True, degree=3)

``````
``````

In [57]:

def root_sum_square(x, y, z):
sum = ((x**2)+(y**2)+(z**2))

def root_mean_square(x, y, z):
mean = ((x**2)+(y**2)+(z**2))/3

def tiltx(x, y, z):
try:
prep = (x/(math.sqrt((y**2)+(z**2))))
tilt = math.atan(prep)
except ZeroDivisionError:
tilt = 0
return tilt

def tilty(x, y, z):
try:
prep = (y/(math.sqrt((x**2)+(z**2))))
tilt = math.atan(prep)
except ZeroDivisionError:
tilt = 0
return tilt

def max_min_diff(max, min):
diff = max - min
return diff

def magnitude(x, y, z):
magnitude = x + y + z
return magnitude

def create_features(df, _window=50):
accel_x = df['ACCEL_X'].astype(float)
accel_y = df['ACCEL_Y'].astype(float)
accel_z = df['ACCEL_Z'].astype(float)
gyro_x = df['GYRO_X'].astype(float)
gyro_y = df['GYRO_Y'].astype(float)
gyro_z = df['GYRO_Z'].astype(float)

df2 = pd.DataFrame()

# Capture stand state here, then average later

df2['stand'] = df['stand'].astype(float)

TIME_SEQUENCE_LENGTH = _window

# Basics

df2['ACCEL_X'] = pd.rolling_mean(accel_x, TIME_SEQUENCE_LENGTH-2, center=True)
df2['ACCEL_Y'] = pd.rolling_mean(accel_y, TIME_SEQUENCE_LENGTH-2, center=True)
df2['ACCEL_Z'] = pd.rolling_mean(accel_z, TIME_SEQUENCE_LENGTH-2, center=True)
df2['GYRO_X'] = pd.rolling_mean(gyro_x, TIME_SEQUENCE_LENGTH-2, center=True)
df2['GYRO_Y'] = pd.rolling_mean(gyro_y, TIME_SEQUENCE_LENGTH-2, center=True)
df2['GYRO_Z'] = pd.rolling_mean(gyro_z, TIME_SEQUENCE_LENGTH-2, center=True)

# standing up detection
df2['avg_stand'] = pd.rolling_mean(df2['stand'], TIME_SEQUENCE_LENGTH-2, center=True)
print df2['avg_stand']

# round standing up as we need it to be either '0' or '1' for training later
df2['avg_stand'] = df2['avg_stand'].apply(lambda x: math.ceil(x))

ol_upper = _window/2
ol_lower = ol_upper-1

new_df = df2[ol_lower::ol_upper] # 50% overlap with 30

return new_df

# Test method:
# data = np.array([np.mean(training_data.ACCEL_X[0:30]), np.mean(training_data.ACCEL_X[30:45]), np.mean(training_data.ACCEL_X[30:60])])
# desired_df = pd.DataFrame(data, columns=columns)
# print desired_df

``````
``````

In [58]:

def set_state(df, state):
"""set the state for training"""

if state == 'your_mount':
df['state'] = 0
elif state == 'your_side_control':
df['state'] = 1
elif state =='your_closed_guard':
df['state'] = 2
elif state =='your_back_control':
df['state'] = 3
elif state =='opponent_mount_or_sc':
df['state'] = 4
elif state =='opponent_closed_guard':
df['state'] = 5
elif state == 'opponent_back_control':
df['state'] = 6
elif state =='non_jj':
df['state'] = 7

return df

``````
``````

In [59]:

def set_stand_state(df, stand_state):
if (stand_state == 1):
df['stand'] = 1
else:
df['stand'] = 0

print df
return df

``````
``````

In [60]:

def combine_setState_createFeatures(directory, state, window=50, stand=0):
"""
convenience method to combine three steps in one function:
(1) combine multiple csv files, (2) set their movement state for training,
(3) combine to create time sequences and add features
"""
combined_data = combine_csv(directory)
combined_data_updated = set_state(combined_data, state)
combined_data_updated2 = set_stand_state(combined_data_updated, stand)
feature_training_data = create_features(combined_data_updated2, window)

``````
``````

In [61]:

def prep(window=30):
"""prepare the raw sensor data"""

ymount_td = combine_setState_createFeatures('your_mount_raw_data', 'your_mount', window, 0)
ysc_td = combine_setState_createFeatures('your_side_control_raw_data', 'your_side_control', window, 0)
ycg_td = combine_setState_createFeatures('your_closed_guard_raw_data', 'your_closed_guard', window, 0)
ybc_td = combine_setState_createFeatures('your_back_control_raw_data', 'your_back_control', window, 0)
#5 Opponent mount or opponent side control
omountsc_td = combine_setState_createFeatures('opponent_mount_and_opponent_side_control_raw_data', 'opponent_mount_or_sc', window, 0)
#6 Opponent closed guard
ocg_td = combine_setState_createFeatures('opponent_closed_guard_raw_data', 'opponent_closed_guard', window, 0)
#7 Opponent back control
obc_td = combine_setState_createFeatures('opponent_back_control_raw_data', 'opponent_back_control', window, 0)
#8 "Non jiu-jitsu" motion
nonjj_td = combine_setState_createFeatures('non_jj_raw_data', 'non_jj', window, 0)
#9 "stand up" motion
stand_up_td = combine_setState_createFeatures('standing_up_raw_data', 'opponent_closed_guard', window, 1)

training_data = concat_data([ymount_td, ysc_td, ycg_td, ybc_td, omountsc_td, ocg_td, obc_td, nonjj_td])
# remove NaN
training_data = blank_filter(training_data)
return training_data

``````
``````

In [62]:

def prep_test(el_file):
el_file = 'data/test_cases/' + el_file
df = pd.DataFrame()
df = resolve_acc_gyro(df)
df = create_features(df)
test_data = blank_filter(df)

return test_data

``````
``````

In [63]:

def test_model_stand(df_train):
"""check model accuracy"""

y = df_train['stand'].values
X = df_train.drop(['stand', 'state', 'index'], axis=1)

if X.isnull().values.any() == False:

rf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_samples_leaf=8, min_samples_split=4,
min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.1)

rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
rf_scores = cross_validation.cross_val_score(
rf, X, df_train.state, cv=10, scoring='accuracy')

print 'rf prediction: {}'.format(accuracy_score(y_test, rf_pred))
print("Random Forest Accuracy: %0.2f (+/- %0.2f)" % (rf_scores.mean(), rf_scores.std() * 2))

importances = rf.feature_importances_
std = np.std([tree.feature_importances_ for tree in rf.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]

# Print the feature ranking
print("Feature ranking:")
for f in range(X.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X.columns[indices[f]], importances[indices[f]]))

``````
``````

In [64]:

def test_model(df_train):
"""check model accuracy"""

y = df_train['state'].values
X = df_train.drop(['state', 'index'], axis=1)

if X.isnull().values.any() == False:

rf = RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_samples_leaf=8, min_samples_split=4,
min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=-1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)

X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.1)

rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
rf_scores = cross_validation.cross_val_score(
rf, X, df_train.state, cv=10, scoring='accuracy')

print 'rf prediction: {}'.format(accuracy_score(y_test, rf_pred))
print("Random Forest Accuracy: %0.2f (+/- %0.2f)" % (rf_scores.mean(), rf_scores.std() * 2))

importances = rf.feature_importances_
std = np.std([tree.feature_importances_ for tree in rf.estimators_],
axis=0)
indices = np.argsort(importances)[::-1]

# Print the feature ranking
print("Feature ranking:")
for f in range(X.shape[1]):
print("%d. feature %s (%f)" % (f + 1, X.columns[indices[f]], importances[indices[f]]))

``````
``````

In [65]:

training_data50 = prep(50)

``````
``````

index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y   GYRO_X   GYRO_Y   GYRO_Z  state  stand
0          0  2016-03-26 13:33:12.67   -0.358    0.636    0.721  2016-03-26 13:33:12.00   21.829 -123.049   -2.622      0      0
1          1  2016-03-26 13:33:12.67   -0.071   -0.115   -1.280  2016-03-26 13:33:12.00   27.012  -63.902  -14.878      0      0
2          2  2016-03-26 13:33:12.67    0.333    0.322   -0.766  2016-03-26 13:33:12.00   19.512  -82.317 -114.451      0      0
3          3  2016-03-26 13:33:12.67    0.015   -0.092   -0.526  2016-03-26 13:33:12.00 -201.768 -148.415 -157.561      0      0
4          4  2016-03-26 13:33:12.67   -0.179   -0.199   -0.809  2016-03-26 13:33:12.00 -128.841  121.524 -129.939      0      0
5          5  2016-03-26 13:33:12.67   -0.010   -0.193   -1.108  2016-03-26 13:33:12.00    9.146  122.622    9.817      0      0
6          6  2016-03-26 13:33:12.67    0.054   -0.190   -1.247  2016-03-26 13:33:12.00   86.159  157.012   79.756      0      0
7          7  2016-03-26 13:33:12.67   -0.081   -0.420   -1.198  2016-03-26 13:33:12.00   16.463   82.622   -8.841      0      0
8          8  2016-03-26 13:33:12.67   -0.299   -0.408   -1.229  2016-03-26 13:33:12.00   -3.841   14.207  -17.378      0      0
9          9  2016-03-26 13:33:12.67   -0.360   -0.201   -0.894  2016-03-26 13:33:13.00   19.390  -39.390  -28.537      0      0
10        10  2016-03-26 13:33:12.67   -0.211   -0.048   -0.570  2016-03-26 13:33:13.00   27.744  -21.159    0.915      0      0
11        11  2016-03-26 13:33:12.67   -0.288   -0.029   -0.955  2016-03-26 13:33:13.00   12.622  -32.805   -1.341      0      0
12        12  2016-03-26 13:33:12.67   -0.383   -0.008   -0.971  2016-03-26 13:33:13.00    5.976  -39.085  -20.061      0      0
13        13  2016-03-26 13:33:12.67   -0.406   -0.162   -0.805  2016-03-26 13:33:13.00   18.963    1.220    2.561      0      0
14        14  2016-03-26 13:33:12.68   -0.349   -0.013   -1.037  2016-03-26 13:33:13.00  -12.744  -17.195   22.256      0      0
15        15  2016-03-26 13:33:12.68   -0.527    0.065   -1.104  2016-03-26 13:33:13.00  -45.427  -70.000   24.695      0      0
16        16  2016-03-26 13:33:12.68   -0.564    0.061   -0.850  2016-03-26 13:33:13.00   -8.476  -44.756   26.220      0      0
17        17  2016-03-26 13:33:12.68   -0.636    0.075   -0.802  2016-03-26 13:33:13.00   -8.780  -16.402   45.854      0      0
18        18  2016-03-26 13:33:12.68   -0.613    0.103   -0.805  2016-03-26 13:33:13.00  -65.854  -61.768   38.415      0      0
19        19  2016-03-26 13:33:12.68   -0.271    0.368   -0.947  2016-03-26 13:33:13.00  -75.488  -74.878   29.756      0      0
20        20  2016-03-26 13:33:12.68   -0.521    0.277   -0.853  2016-03-26 13:33:13.00  -24.207   -0.976    3.780      0      0
21        21  2016-03-26 13:33:12.68   -0.545    0.287   -0.898  2016-03-26 13:33:13.00    4.024  -29.878  -46.829      0      0
22        22  2016-03-26 13:33:12.68   -0.421    0.125   -0.978  2016-03-26 13:33:13.00   -0.549  -16.159  -22.927      0      0
23        23  2016-03-26 13:33:12.68   -0.408   -0.005   -0.533  2016-03-26 13:33:13.00  -33.598  -11.524  -44.634      0      0
24        24  2016-03-26 13:33:12.68   -0.304   -0.068   -0.863  2016-03-26 13:33:13.00  -29.146    8.963  -56.280      0      0
25        25  2016-03-26 13:33:12.68   -0.398   -0.079   -1.206  2016-03-26 13:33:13.00  -16.524   -8.598  -45.244      0      0
26        26  2016-03-26 13:33:12.68   -0.368   -0.035   -1.088  2016-03-26 13:33:13.00   22.378   12.012  -40.793      0      0
27        27  2016-03-26 13:33:12.68   -0.234    0.004   -0.950  2016-03-26 13:33:13.00   45.671   77.622  -21.037      0      0
28        28  2016-03-26 13:33:12.68   -0.207    0.200   -0.881  2016-03-26 13:33:13.00  -10.427  -14.817  -34.573      0      0
29        29  2016-03-26 13:33:12.69   -0.323   -0.155   -0.934  2016-03-26 13:33:13.00  -31.890  -34.085  -27.256      0      0
...      ...                     ...      ...      ...      ...                     ...      ...      ...      ...    ...    ...
16210   2568  2016-03-12 14:04:58.36   -0.899    0.180   -0.160  2016-03-12 14:04:58.96   17.988  -37.927  -16.280      0      0
16211   2569  2016-03-12 14:04:58.36   -0.974    0.180   -0.264  2016-03-12 14:04:58.96   42.439  -24.634  -10.854      0      0
16212   2570  2016-03-12 14:04:58.36   -0.973    0.136   -0.166  2016-03-12 14:04:58.96   26.220  -15.122   -1.220      0      0
16213   2571  2016-03-12 14:04:58.36   -1.021    0.164   -0.085  2016-03-12 14:04:58.96   13.110  -28.293   16.098      0      0
16214   2572  2016-03-12 14:04:58.36   -1.034    0.120   -0.189  2016-03-12 14:04:58.96   30.793  -17.683   36.037      0      0
16215   2573  2016-03-12 14:04:58.36   -0.898    0.193   -0.115  2016-03-12 14:04:58.96   33.659  -10.549   37.256      0      0
16216   2574  2016-03-12 14:04:58.36   -0.902    0.167   -0.205  2016-03-12 14:04:58.96   29.024   -5.854   23.110      0      0
16217   2575  2016-03-12 14:04:58.36   -0.965    0.114   -0.245  2016-03-12 14:04:58.96   17.988  -22.256   17.317      0      0
16218   2576  2016-03-12 14:04:58.36   -0.977    0.156   -0.207  2016-03-12 14:04:58.96   26.524  -19.817   10.488      0      0
16219   2577  2016-03-12 14:04:58.36   -1.026    0.101   -0.257  2016-03-12 14:04:58.96   21.280    6.585    6.524      0      0
16220   2578  2016-03-12 14:04:58.36   -1.055   -0.042   -0.335  2016-03-12 14:04:58.96    8.963  -12.317    2.927      0      0
16221   2579  2016-03-12 14:04:58.36   -1.057   -0.042   -0.427  2016-03-12 14:04:58.96   24.451   11.890   -8.049      0      0
16222   2580  2016-03-12 14:04:58.36   -1.042   -0.002   -0.297  2016-03-12 14:04:58.96   10.366   14.695   -7.622      0      0
16223   2581  2016-03-12 14:04:58.36   -1.006   -0.052   -0.353  2016-03-12 14:04:58.96    8.232   25.305  -10.915      0      0
16224   2582  2016-03-12 14:04:58.36   -1.006   -0.028   -0.438  2016-03-12 14:04:58.96   18.720   38.110  -15.915      0      0
16225   2583  2016-03-12 14:04:58.36   -0.952   -0.022   -0.451  2016-03-12 14:04:58.96   11.524   39.146  -16.707      0      0
16226   2584  2016-03-12 14:04:58.36   -0.925   -0.033   -0.450  2016-03-12 14:04:58.96    5.732   34.756  -19.207      0      0
16227   2585  2016-03-12 14:04:58.36   -0.877   -0.012   -0.511  2016-03-12 14:04:58.96    1.585   40.183  -20.976      0      0
16228   2586  2016-03-12 14:04:58.36   -0.898   -0.009   -0.593  2016-03-12 14:04:58.96    7.317   40.854  -22.927      0      0
16229   2587  2016-03-12 14:04:58.37   -0.927    0.131   -0.613  2016-03-12 14:04:58.96   -3.171   27.195  -19.207      0      0
16230   2588  2016-03-12 14:04:58.37   -0.894    0.101   -0.598  2016-03-12 14:04:58.96  -12.805   21.890  -18.720      0      0
16231   2589  2016-03-12 14:04:58.37   -0.884    0.100   -0.548  2016-03-12 14:04:58.96  -25.183   18.171  -14.756      0      0
16232   2590  2016-03-12 14:04:58.37   -0.900    0.013   -0.681  2016-03-12 14:04:58.96  -22.012   12.073  -13.598      0      0
16233   2591  2016-03-12 14:04:58.37   -0.826    0.043   -0.607  2016-03-12 14:04:58.96  -31.829    9.817  -10.610      0      0
16234   2592                     NaN      NaN      NaN      NaN  2016-03-12 14:04:58.96  -32.195   15.122   -6.585      0      0
16235   2593                     NaN      NaN      NaN      NaN  2016-03-12 14:04:58.96  -16.951   17.134   -1.341      0      0
16236   2594                     NaN      NaN      NaN      NaN  2016-03-12 14:04:58.96   -3.476   20.549   17.256      0      0
16237   2595                     NaN      NaN      NaN      NaN  2016-03-12 14:04:58.96   10.549   14.634   27.744      0      0
16238   2596                     NaN      NaN      NaN      NaN  2016-03-12 14:04:58.96    1.951    3.659   34.634      0      0
16239   2597                     NaN      NaN      NaN      NaN  2016-03-12 14:04:58.96   13.963    8.110   36.585      0      0

[16240 rows x 11 columns]
0       NaN
1       NaN
2       NaN
3       NaN
4       NaN
5       NaN
6       NaN
7       NaN
8       NaN
9       NaN
10      NaN
11      NaN
12      NaN
13      NaN
14      NaN
15      NaN
16      NaN
17      NaN
18      NaN
19      NaN
20      NaN
21      NaN
22      NaN
23      NaN
24        0
25        0
26        0
27        0
28        0
29        0
..
16210     0
16211     0
16212     0
16213     0
16214     0
16215     0
16216     0
16217   NaN
16218   NaN
16219   NaN
16220   NaN
16221   NaN
16222   NaN
16223   NaN
16224   NaN
16225   NaN
16226   NaN
16227   NaN
16228   NaN
16229   NaN
16230   NaN
16231   NaN
16232   NaN
16233   NaN
16234   NaN
16235   NaN
16236   NaN
16237   NaN
16238   NaN
16239   NaN
Name: avg_stand, dtype: float64

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y  GYRO_X   GYRO_Y  GYRO_Z  state  stand
0         0  2016-03-26 13:29:58.62   -0.646   -0.181   -0.770  2016-03-26 13:29:58.81  55.915   41.098 -11.829      1      0
1         1  2016-03-26 13:29:58.62   -0.841   -0.167   -0.888  2016-03-26 13:29:58.81  51.037   26.159 -10.976      1      0
2         2  2016-03-26 13:29:58.62   -0.788   -0.104   -0.893  2016-03-26 13:29:58.81  25.671  -28.110 -12.073      1      0
3         3  2016-03-26 13:29:58.62   -0.623   -0.141   -0.765  2016-03-26 13:29:58.81 -18.293  -49.512   6.829      1      0
4         4  2016-03-26 13:29:58.62   -0.512   -0.071   -0.807  2016-03-26 13:29:58.81 -46.098  -39.817   6.890      1      0
5         5  2016-03-26 13:29:58.62   -0.574   -0.069   -0.843  2016-03-26 13:29:58.81 -48.415  -81.646  -3.659      1      0
6         6  2016-03-26 13:29:58.62   -0.424   -0.068   -0.808  2016-03-26 13:29:58.81 -33.171 -128.293 -14.329      1      0
7         7  2016-03-26 13:29:58.62   -0.298   -0.133   -1.026  2016-03-26 13:29:58.81 -20.732  -62.805 -22.317      1      0
8         8  2016-03-26 13:29:58.62   -0.212   -0.249   -0.926  2016-03-26 13:29:58.81 -50.183   42.317 -13.354      1      0
9         9  2016-03-26 13:29:58.62   -0.110   -0.159   -0.926  2016-03-26 13:29:58.81 -37.805   90.427 -21.707      1      0
10       10  2016-03-26 13:29:58.62   -0.035    0.135   -0.896  2016-03-26 13:29:58.81 -22.927  121.585 -35.732      1      0
11       11  2016-03-26 13:29:58.62   -0.153    0.133   -0.959  2016-03-26 13:29:58.81   2.866   87.073 -36.524      1      0
12       12  2016-03-26 13:29:58.62   -0.227    0.002   -0.955  2016-03-26 13:29:58.81  -6.829   37.622   1.402      1      0
13       13  2016-03-26 13:29:58.62   -0.309    0.034   -1.001  2016-03-26 13:29:58.81 -24.756   86.463  11.220      1      0
14       14  2016-03-26 13:29:58.62   -0.301    0.084   -0.980  2016-03-26 13:29:58.81 -63.476   94.085   4.573      1      0
15       15  2016-03-26 13:29:58.62   -0.228    0.131   -1.041  2016-03-26 13:29:58.81 -70.549   51.524  -2.500      1      0
16       16  2016-03-26 13:29:58.62   -0.386    0.041   -0.878  2016-03-26 13:29:58.81 -45.976   14.268   5.305      1      0
17       17  2016-03-26 13:29:58.62   -0.512    0.082   -0.894  2016-03-26 13:29:58.81  -9.390  -15.854  -7.439      1      0
18       18  2016-03-26 13:29:58.62   -0.455    0.118   -0.841  2016-03-26 13:29:58.81  18.659  -24.939 -34.024      1      0
19       19  2016-03-26 13:29:58.62   -0.319    0.038   -0.884  2016-03-26 13:29:58.81  17.378  -24.695 -44.878      1      0
20       20  2016-03-26 13:29:58.62   -0.328   -0.124   -0.850  2016-03-26 13:29:58.81  31.707  -57.500  -2.561      1      0
21       21  2016-03-26 13:29:58.63   -0.600   -0.208   -0.866  2016-03-26 13:29:58.81  34.207  -64.634  27.073      1      0
22       22  2016-03-26 13:29:58.63   -0.664   -0.279   -0.900  2016-03-26 13:29:58.81  22.256  -54.634  12.805      1      0
23       23  2016-03-26 13:29:58.63   -0.679   -0.206   -0.838  2016-03-26 13:29:58.81   0.183   -5.732   2.561      1      0
24       24  2016-03-26 13:29:58.63   -0.589   -0.193   -0.839  2016-03-26 13:29:58.81  12.683  -25.488  -7.866      1      0
25       25  2016-03-26 13:29:58.63   -0.565   -0.110   -0.792  2016-03-26 13:29:58.81  14.268  -52.988 -30.671      1      0
26       26  2016-03-26 13:29:58.63   -0.440    0.047   -0.930  2016-03-26 13:29:58.81  -3.598   -5.671 -40.061      1      0
27       27  2016-03-26 13:29:58.63   -0.393   -0.027   -0.980  2016-03-26 13:29:58.81   5.061   33.780 -55.976      1      0
28       28  2016-03-26 13:29:58.63   -0.386   -0.133   -0.873  2016-03-26 13:29:58.82 -56.646   26.951 -12.500      1      0
29       29  2016-03-26 13:29:58.63   -0.434   -0.096   -0.890  2016-03-26 13:29:58.82 -26.463   15.000  19.146      1      0
...     ...                     ...      ...      ...      ...                     ...     ...      ...     ...    ...    ...
9322   2493  2016-03-12 13:53:17.76   -0.375   -0.166   -0.946  2016-03-12 13:53:18.32  58.963  -26.280 -13.476      1      0
9323   2494  2016-03-12 13:53:17.76   -0.273   -0.228   -1.115  2016-03-12 13:53:18.32  25.793  -25.366  -4.268      1      0
9324   2495  2016-03-12 13:53:17.76   -0.276   -0.239   -1.172  2016-03-12 13:53:18.32  17.805  -13.537   0.793      1      0
9325   2496  2016-03-12 13:53:17.76   -0.694   -0.384   -1.174  2016-03-12 13:53:18.32  16.707   -1.220  -0.488      1      0
9326   2497  2016-03-12 13:53:17.76   -0.536   -0.201   -1.057  2016-03-12 13:53:18.32  18.902    5.610  -5.183      1      0
9327   2498  2016-03-12 13:53:17.76   -0.081   -0.157   -0.753  2016-03-12 13:53:18.32  21.402    5.305  -8.171      1      0
9328   2499  2016-03-12 13:53:17.76   -0.443   -0.370   -0.635  2016-03-12 13:53:18.32  14.573    5.061  -8.232      1      0
9329   2500  2016-03-12 13:53:17.76   -0.493   -0.455   -0.773  2016-03-12 13:53:18.32  15.244   11.585  -4.634      1      0
9330   2501  2016-03-12 13:53:17.76   -0.521   -0.370   -0.594  2016-03-12 13:53:18.32  13.476    0.366  -3.354      1      0
9331   2502  2016-03-12 13:53:17.76   -0.582   -0.724   -0.420                     NaN     NaN      NaN     NaN      1      0
9332   2503  2016-03-12 13:53:17.76   -0.685   -0.695   -0.274                     NaN     NaN      NaN     NaN      1      0
9333   2504  2016-03-12 13:53:17.76   -0.647   -0.560   -0.220                     NaN     NaN      NaN     NaN      1      0
9334   2505  2016-03-12 13:53:17.76   -0.678   -0.510   -0.064                     NaN     NaN      NaN     NaN      1      0
9335   2506  2016-03-12 13:53:17.76   -0.887   -0.690   -0.018                     NaN     NaN      NaN     NaN      1      0
9336   2507  2016-03-12 13:53:17.76   -0.792   -0.608   -0.073                     NaN     NaN      NaN     NaN      1      0
9337   2508  2016-03-12 13:53:17.76   -0.756   -0.611   -0.073                     NaN     NaN      NaN     NaN      1      0
9338   2509  2016-03-12 13:53:17.76   -0.779   -0.619   -0.007                     NaN     NaN      NaN     NaN      1      0
9339   2510  2016-03-12 13:53:17.77   -0.833   -0.573    0.075                     NaN     NaN      NaN     NaN      1      0
9340   2511  2016-03-12 13:53:17.77   -0.869   -0.651    0.097                     NaN     NaN      NaN     NaN      1      0
9341   2512  2016-03-12 13:53:17.77   -0.823   -0.670    0.140                     NaN     NaN      NaN     NaN      1      0
9342   2513  2016-03-12 13:53:17.77   -0.747   -0.626    0.172                     NaN     NaN      NaN     NaN      1      0
9343   2514  2016-03-12 13:53:17.77   -0.755   -0.433    0.396                     NaN     NaN      NaN     NaN      1      0
9344   2515  2016-03-12 13:53:17.77   -0.725   -0.525    0.336                     NaN     NaN      NaN     NaN      1      0
9345   2516  2016-03-12 13:53:17.77   -0.656   -0.585    0.273                     NaN     NaN      NaN     NaN      1      0
9346   2517  2016-03-12 13:53:17.77   -0.692   -0.620    0.258                     NaN     NaN      NaN     NaN      1      0
9347   2518  2016-03-12 13:53:17.77   -0.823   -0.662    0.314                     NaN     NaN      NaN     NaN      1      0
9348   2519  2016-03-12 13:53:17.77   -0.822   -0.558    0.316                     NaN     NaN      NaN     NaN      1      0
9349   2520  2016-03-12 13:53:17.77   -0.807   -0.453    0.350                     NaN     NaN      NaN     NaN      1      0
9350   2521  2016-03-12 13:53:17.77   -0.839   -0.525    0.444                     NaN     NaN      NaN     NaN      1      0
9351   2522  2016-03-12 13:53:17.77   -0.833   -0.617    0.514                     NaN     NaN      NaN     NaN      1      0

[9352 rows x 11 columns]
0      NaN
1      NaN
2      NaN
3      NaN
4      NaN
5      NaN
6      NaN
7      NaN
8      NaN
9      NaN
10     NaN
11     NaN
12     NaN
13     NaN
14     NaN
15     NaN
16     NaN
17     NaN
18     NaN
19     NaN
20     NaN
21     NaN
22     NaN
23     NaN
24       0
25       0
26       0
27       0
28       0
29       0
..
9322     0
9323     0
9324     0
9325     0
9326     0
9327     0
9328     0
9329   NaN
9330   NaN
9331   NaN
9332   NaN
9333   NaN
9334   NaN
9335   NaN
9336   NaN
9337   NaN
9338   NaN
9339   NaN
9340   NaN
9341   NaN
9342   NaN
9343   NaN
9344   NaN
9345   NaN
9346   NaN
9347   NaN
9348   NaN
9349   NaN
9350   NaN
9351   NaN
Name: avg_stand, dtype: float64
index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y  GYRO_X  GYRO_Y   GYRO_Z  state  stand
0          0  2016-03-26 13:35:45.96   -0.198   -0.284    1.258  2016-03-26 13:35:46.36 -90.244 -95.427  -15.793      2      0
1          1  2016-03-26 13:35:45.96   -0.161   -0.292    1.031  2016-03-26 13:35:46.36 -85.305 -93.780  -10.976      2      0
2          2  2016-03-26 13:35:45.96   -0.240   -0.421    1.090  2016-03-26 13:35:46.36 -70.305 -79.695   -5.854      2      0
3          3  2016-03-26 13:35:45.96    0.201   -0.567    0.981  2016-03-26 13:35:46.36 -48.110 -77.805    5.305      2      0
4          4  2016-03-26 13:35:45.96    0.282   -0.669    0.791  2016-03-26 13:35:46.36  -2.561 -58.293   41.220      2      0
5          5  2016-03-26 13:35:45.96    0.013   -0.529    0.777  2016-03-26 13:35:46.36 -17.256 -42.073   20.183      2      0
6          6  2016-03-26 13:35:45.96   -0.195   -0.424    0.867  2016-03-26 13:35:46.36 -21.159 -12.683    7.378      2      0
7          7  2016-03-26 13:35:45.96   -0.182   -0.536    0.968  2016-03-26 13:35:46.36  29.695 -26.341    4.695      2      0
8          8  2016-03-26 13:35:45.96   -0.155   -0.401    0.963  2016-03-26 13:35:46.36  26.585 -16.524   -8.963      2      0
9          9  2016-03-26 13:35:45.96   -0.127   -0.344    1.004  2016-03-26 13:35:46.36 -18.293  14.878  -34.817      2      0
10        10  2016-03-26 13:35:45.96   -0.234   -0.354    0.969  2016-03-26 13:35:46.36 -16.890  23.659   -7.561      2      0
11        11  2016-03-26 13:35:45.96   -0.335   -0.516    0.900  2016-03-26 13:35:46.36  -3.293  23.415   23.232      2      0
12        12  2016-03-26 13:35:45.96   -0.405   -0.511    0.943  2016-03-26 13:35:46.36   4.512  20.305   29.207      2      0
13        13  2016-03-26 13:35:45.96   -0.423   -0.548    0.923  2016-03-26 13:35:46.36   0.915  26.768   24.085      2      0
14        14  2016-03-26 13:35:45.96   -0.274   -0.409    0.799  2016-03-26 13:35:46.36 -11.646  29.024   29.024      2      0
15        15  2016-03-26 13:35:45.96   -0.500   -0.293    0.807  2016-03-26 13:35:46.36 -28.049  25.488   41.280      2      0
16        16  2016-03-26 13:35:45.96   -0.562   -0.259    0.865  2016-03-26 13:35:46.36 -16.829  23.110   32.683      2      0
17        17  2016-03-26 13:35:45.96   -0.514   -0.232    0.869  2016-03-26 13:35:46.36  11.098  12.500   44.024      2      0
18        18  2016-03-26 13:35:45.96   -0.468   -0.085    0.988  2016-03-26 13:35:46.36  37.317 -12.988   67.866      2      0
19        19  2016-03-26 13:35:45.96   -0.267    0.055    0.955  2016-03-26 13:35:46.36  41.646  -5.976   54.146      2      0
20        20  2016-03-26 13:35:45.96   -0.378   -0.071    1.016  2016-03-26 13:35:46.36  32.012 -13.841   64.878      2      0
21        21  2016-03-26 13:35:45.96   -0.376   -0.165    0.940  2016-03-26 13:35:46.36  11.951 -24.268   74.451      2      0
22        22  2016-03-26 13:35:45.96   -0.172   -0.092    0.890  2016-03-26 13:35:46.36  11.402 -14.512   95.610      2      0
23        23  2016-03-26 13:35:45.96   -0.256    0.049    0.956  2016-03-26 13:35:46.36   9.695   1.524  100.183      2      0
24        24  2016-03-26 13:35:45.96   -0.253    0.024    0.967  2016-03-26 13:35:46.36   7.561 -19.573   74.817      2      0
25        25  2016-03-26 13:35:45.97   -0.083    0.236    1.098  2016-03-26 13:35:46.36  19.329  -5.671   98.354      2      0
26        26  2016-03-26 13:35:45.97   -0.101    0.299    1.193  2016-03-26 13:35:46.36  29.085  19.146   77.988      2      0
27        27  2016-03-26 13:35:45.97   -0.161    0.208    1.058  2016-03-26 13:35:46.36  44.207 -26.159   19.207      2      0
28        28  2016-03-26 13:35:45.97   -0.691   -0.009    1.077  2016-03-26 13:35:46.36  58.049 -51.829    1.098      2      0
29        29  2016-03-26 13:35:45.97   -0.055    0.145    0.917  2016-03-26 13:35:46.36  44.634 -46.037  -16.220      2      0
...      ...                     ...      ...      ...      ...                     ...     ...     ...      ...    ...    ...
12790    831  2016-03-12 13:38:52.05   -0.137   -0.250    1.016  2016-03-12 13:38:52.25 -79.268  15.122  -36.585      2      0
12791    832  2016-03-12 13:38:52.05   -0.251   -0.154    0.975  2016-03-12 13:38:52.25 -68.232  17.622  -36.524      2      0
12792    833  2016-03-12 13:38:52.05   -0.291   -0.304    0.979  2016-03-12 13:38:52.25 -51.280  12.500  -38.780      2      0
12793    834  2016-03-12 13:38:52.05   -0.285   -0.360    0.923  2016-03-12 13:38:52.25 -43.171  17.256  -37.134      2      0
12794    835  2016-03-12 13:38:52.05   -0.316   -0.402    0.907  2016-03-12 13:38:52.25 -38.963  24.878  -33.963      2      0
12795    836  2016-03-12 13:38:52.05   -0.269   -0.363    0.947  2016-03-12 13:38:52.25 -33.963  27.012  -30.671      2      0
12796    837  2016-03-12 13:38:52.05   -0.237   -0.431    0.878  2016-03-12 13:38:52.25 -42.988  36.524  -28.049      2      0
12797    838  2016-03-12 13:38:52.05   -0.254   -0.431    0.914  2016-03-12 13:38:52.25 -40.854  38.720  -27.439      2      0
12798    839  2016-03-12 13:38:52.05   -0.269   -0.425    0.960  2016-03-12 13:38:52.25 -34.939  37.927  -26.951      2      0
12799    840  2016-03-12 13:38:52.05   -0.241   -0.423    0.888  2016-03-12 13:38:52.25 -42.012  30.305  -26.463      2      0
12800    841  2016-03-12 13:38:52.05   -0.255   -0.431    0.912  2016-03-12 13:38:52.25 -49.024  26.280  -17.561      2      0
12801    842  2016-03-12 13:38:52.05   -0.205   -0.378    0.945  2016-03-12 13:38:52.25 -40.366  17.866   -8.902      2      0
12802    843  2016-03-12 13:38:52.05   -0.253   -0.351    0.905  2016-03-12 13:38:52.25 -32.439  13.659   -2.134      2      0
12803    844  2016-03-12 13:38:52.05   -0.274   -0.314    0.921  2016-03-12 13:38:52.25 -27.866  16.463   -3.110      2      0
12804    845  2016-03-12 13:38:52.05   -0.251   -0.351    0.933  2016-03-12 13:38:52.25 -26.646   9.085   -3.293      2      0
12805    846  2016-03-12 13:38:52.05   -0.261   -0.362    0.893  2016-03-12 13:38:52.25 -17.378   7.073   -0.976      2      0
12806    847  2016-03-12 13:38:52.05   -0.304   -0.314    0.926  2016-03-12 13:38:52.25 -15.000   6.098    3.476      2      0
12807    848  2016-03-12 13:38:52.05   -0.308   -0.314    0.940  2016-03-12 13:38:52.25 -10.122  -6.280    5.488      2      0
12808    849  2016-03-12 13:38:52.05   -0.267   -0.216    0.976  2016-03-12 13:38:52.25  12.622 -17.134    7.439      2      0
12809    850  2016-03-12 13:38:52.05   -0.226   -0.259    1.048  2016-03-12 13:38:52.25  21.402 -18.902    5.488      2      0
12810    851  2016-03-12 13:38:52.05   -0.127   -0.195    0.984  2016-03-12 13:38:52.25  19.573 -13.902    4.329      2      0
12811    852  2016-03-12 13:38:52.05   -0.210   -0.184    1.012  2016-03-12 13:38:52.25  21.098  -8.659    8.171      2      0
12812    853                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  21.220 -10.183    8.476      2      0
12813    854                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  24.634 -13.049    5.854      2      0
12814    855                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  36.768 -16.951    4.756      2      0
12815    856                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  36.098 -19.878    6.037      2      0
12816    857                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  40.000 -26.402    6.524      2      0
12817    858                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  43.963 -29.695   11.280      2      0
12818    859                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  34.207 -30.488    7.683      2      0
12819    860                     NaN      NaN      NaN      NaN  2016-03-12 13:38:52.25  34.939 -32.561   17.988      2      0

[12820 rows x 11 columns]
0       NaN
1       NaN
2       NaN
3       NaN
4       NaN
5       NaN
6       NaN
7       NaN
8       NaN
9       NaN
10      NaN
11      NaN
12      NaN
13      NaN
14      NaN
15      NaN
16      NaN
17      NaN
18      NaN
19      NaN
20      NaN
21      NaN
22      NaN
23      NaN
24        0
25        0
26        0
27        0
28        0
29        0
..
12790     0
12791     0
12792     0
12793     0
12794     0
12795     0
12796     0
12797   NaN
12798   NaN
12799   NaN
12800   NaN
12801   NaN
12802   NaN
12803   NaN
12804   NaN
12805   NaN
12806   NaN
12807   NaN
12808   NaN
12809   NaN
12810   NaN
12811   NaN
12812   NaN
12813   NaN
12814   NaN
12815   NaN
12816   NaN
12817   NaN
12818   NaN
12819   NaN
Name: avg_stand, dtype: float64
index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y  GYRO_X  GYRO_Y  GYRO_Z  state  stand
0         0  2016-03-15 20:31:42.26   -0.967   -0.336    0.011  2016-03-15 20:31:42.46   1.280  11.829  -2.439      3      0
1         1  2016-03-15 20:31:42.26   -0.993   -0.332   -0.021  2016-03-15 20:31:42.46  -0.732  11.159  -3.476      3      0
2         2  2016-03-15 20:31:42.26   -0.979   -0.343   -0.017  2016-03-15 20:31:42.46  -3.780   8.293  -3.354      3      0
3         3  2016-03-15 20:31:42.26   -0.961   -0.342   -0.014  2016-03-15 20:31:42.46  -6.037   2.439  -1.768      3      0
4         4  2016-03-15 20:31:42.26   -0.963   -0.357   -0.020  2016-03-15 20:31:42.46  -5.305  -0.732  -0.915      3      0
5         5  2016-03-15 20:31:42.26   -0.962   -0.368   -0.009  2016-03-15 20:31:42.46  -2.134  -6.646   0.244      3      0
6         6  2016-03-15 20:31:42.26   -0.951   -0.368   -0.002  2016-03-15 20:31:42.46  -0.793 -11.585  -0.061      3      0
7         7  2016-03-15 20:31:42.26   -0.959   -0.361    0.004  2016-03-15 20:31:42.46   0.122 -12.500  -0.671      3      0
8         8  2016-03-15 20:31:42.26   -0.962   -0.363    0.010  2016-03-15 20:31:42.47   0.183 -11.646  -1.037      3      0
9         9  2016-03-15 20:31:42.26   -0.953   -0.362    0.022  2016-03-15 20:31:42.47  -0.244 -12.744  -1.220      3      0
10       10  2016-03-15 20:31:42.26   -0.966   -0.354    0.044  2016-03-15 20:31:42.47  -0.122 -11.402  -1.220      3      0
11       11  2016-03-15 20:31:42.26   -0.959   -0.363    0.046  2016-03-15 20:31:42.47  -0.488  -7.683  -1.098      3      0
12       12  2016-03-15 20:31:42.26   -0.955   -0.361    0.047  2016-03-15 20:31:42.47   1.098  -7.378  -1.220      3      0
13       13  2016-03-15 20:31:42.26   -0.945   -0.360    0.055  2016-03-15 20:31:42.47   1.951  -3.537   0.427      3      0
14       14  2016-03-15 20:31:42.26   -0.965   -0.354    0.061  2016-03-15 20:31:42.47   2.500  -3.902   1.341      3      0
15       15  2016-03-15 20:31:42.26   -0.959   -0.361    0.061  2016-03-15 20:31:42.47   0.915  -1.646   0.854      3      0
16       16  2016-03-15 20:31:42.26   -0.946   -0.363    0.041  2016-03-15 20:31:42.47   0.488  -3.963   0.976      3      0
17       17  2016-03-15 20:31:42.26   -0.952   -0.323    0.057  2016-03-15 20:31:42.47   1.341  -2.439   0.976      3      0
18       18  2016-03-15 20:31:42.26   -0.965   -0.346    0.052  2016-03-15 20:31:42.47  -0.488  -0.183   1.524      3      0
19       19  2016-03-15 20:31:42.26   -0.939   -0.375    0.049  2016-03-15 20:31:42.47   0.244   0.976   3.476      3      0
20       20  2016-03-15 20:31:42.26   -0.966   -0.363    0.070  2016-03-15 20:31:42.47   4.207   5.305   1.037      3      0
21       21  2016-03-15 20:31:42.26   -0.985   -0.321    0.064  2016-03-15 20:31:42.47  11.280   5.915  -5.732      3      0
22       22  2016-03-15 20:31:42.26   -1.018   -0.248    0.137  2016-03-15 20:31:42.47  10.976   0.427 -13.171      3      0
23       23  2016-03-15 20:31:42.26   -0.961   -0.376    0.064  2016-03-15 20:31:42.47  -2.195 -10.610 -19.939      3      0
24       24  2016-03-15 20:31:42.26   -0.936   -0.443    0.108  2016-03-15 20:31:42.47 -28.902  16.098  -9.085      3      0
25       25  2016-03-15 20:31:42.27   -0.888   -0.444    0.062  2016-03-15 20:31:42.47 -15.793  17.561  -1.463      3      0
26       26  2016-03-15 20:31:42.27   -0.874   -0.466    0.085  2016-03-15 20:31:42.47 -12.622  15.305   0.793      3      0
27       27  2016-03-15 20:31:42.27   -0.889   -0.481    0.054  2016-03-15 20:31:42.47 -10.671  15.793   0.427      3      0
28       28  2016-03-15 20:31:42.27   -0.963   -0.416   -0.187  2016-03-15 20:31:42.47  -6.037  12.500   2.988      3      0
29       29  2016-03-15 20:31:42.27   -0.978   -0.449   -0.007  2016-03-15 20:31:42.47  59.695  31.829   1.280      3      0
...     ...                     ...      ...      ...      ...                     ...     ...     ...     ...    ...    ...
5893   1170  2016-03-12 13:24:23.61   -0.005    0.868    0.510  2016-03-12 13:24:23.87   2.012   6.463   2.012      3      0
5894   1171  2016-03-12 13:24:23.61    0.024    0.869    0.522  2016-03-12 13:24:23.87   6.524  10.549   1.098      3      0
5895   1172  2016-03-12 13:24:23.61   -0.007    0.905    0.518  2016-03-12 13:24:23.87   3.354  13.232  -0.610      3      0
5896   1173  2016-03-12 13:24:23.61    0.050    0.727    0.581  2016-03-12 13:24:23.87  -1.951  15.244   0.061      3      0
5897   1174  2016-03-12 13:24:23.61    0.085    0.730    0.556  2016-03-12 13:24:23.87   0.488   6.585  -1.159      3      0
5898   1175  2016-03-12 13:24:23.61    0.054    0.793    0.581  2016-03-12 13:24:23.87  -4.207   6.890  -7.866      3      0
5899   1176  2016-03-12 13:24:23.61    0.021    0.860    0.534  2016-03-12 13:24:23.88   0.244   3.293 -11.220      3      0
5900   1177  2016-03-12 13:24:23.61    0.016    0.841    0.523  2016-03-12 13:24:23.88   0.854  -2.866  -9.390      3      0
5901   1178  2016-03-12 13:24:23.61   -0.015    0.826    0.583  2016-03-12 13:24:23.88  -1.524  -4.634 -11.768      3      0
5902   1179  2016-03-12 13:24:23.61    0.002    0.792    0.573  2016-03-12 13:24:23.88   2.927 -10.183  -7.317      3      0
5903   1180  2016-03-12 13:24:23.61    0.022    0.797    0.546  2016-03-12 13:24:23.88  -3.476 -10.671  -2.134      3      0
5904   1181  2016-03-12 13:24:23.61    0.043    0.851    0.567  2016-03-12 13:24:23.88  -9.939  -5.122   0.976      3      0
5905   1182  2016-03-12 13:24:23.61   -0.019    0.820    0.532  2016-03-12 13:24:23.88  -9.878  -5.305  -0.610      3      0
5906   1183  2016-03-12 13:24:23.61   -0.045    0.847    0.520  2016-03-12 13:24:23.88  -9.207  -2.683  -0.061      3      0
5907   1184  2016-03-12 13:24:23.61   -0.036    0.858    0.512  2016-03-12 13:24:23.88  -7.195   0.671   1.768      3      0
5908   1185  2016-03-12 13:24:23.61   -0.047    0.870    0.547  2016-03-12 13:24:23.88  -6.646   2.805   2.561      3      0
5909   1186  2016-03-12 13:24:23.62   -0.074    0.835    0.579  2016-03-12 13:24:23.88  -1.829   5.488   2.500      3      0
5910   1187  2016-03-12 13:24:23.62   -0.024    0.812    0.580  2016-03-12 13:24:23.88   1.524   0.854   0.244      3      0
5911   1188  2016-03-12 13:24:23.62    0.014    0.815    0.571  2016-03-12 13:24:23.88   3.049  -7.012  -1.280      3      0
5912   1189  2016-03-12 13:24:23.62    0.021    0.796    0.559  2016-03-12 13:24:23.88   2.683  -4.756   0.122      3      0
5913   1190  2016-03-12 13:24:23.62   -0.016    0.823    0.593  2016-03-12 13:24:23.88  -0.488  -0.183   3.537      3      0
5914   1191  2016-03-12 13:24:23.62   -0.064    0.779    0.605  2016-03-12 13:24:23.88  -1.220   2.073   4.207      3      0
5915   1192  2016-03-12 13:24:23.62   -0.026    0.812    0.601  2016-03-12 13:24:23.88   8.049   0.305   2.317      3      0
5916   1193  2016-03-12 13:24:23.62   -0.038    0.825    0.596  2016-03-12 13:24:23.88  35.671 -13.232   2.866      3      0
5917   1194  2016-03-12 13:24:23.62    0.002    0.812    0.596                     NaN     NaN     NaN     NaN      3      0
5918   1195  2016-03-12 13:24:23.62    0.004    0.815    0.567                     NaN     NaN     NaN     NaN      3      0
5919   1196  2016-03-12 13:24:23.62    0.006    0.792    0.587                     NaN     NaN     NaN     NaN      3      0
5920   1197  2016-03-12 13:24:23.62    0.021    0.793    0.571                     NaN     NaN     NaN     NaN      3      0
5921   1198  2016-03-12 13:24:23.62    0.013    0.825    0.542                     NaN     NaN     NaN     NaN      3      0
5922   1199  2016-03-12 13:24:23.62    0.015    0.834    0.522                     NaN     NaN     NaN     NaN      3      0

[5923 rows x 11 columns]
0      NaN
1      NaN
2      NaN
3      NaN
4      NaN
5      NaN
6      NaN
7      NaN
8      NaN
9      NaN
10     NaN
11     NaN
12     NaN
13     NaN
14     NaN
15     NaN
16     NaN
17     NaN
18     NaN
19     NaN
20     NaN
21     NaN
22     NaN
23     NaN
24       0
25       0
26       0
27       0
28       0
29       0
..
5893     0
5894     0
5895     0
5896     0
5897     0
5898     0
5899     0
5900   NaN
5901   NaN
5902   NaN
5903   NaN
5904   NaN
5905   NaN
5906   NaN
5907   NaN
5908   NaN
5909   NaN
5910   NaN
5911   NaN
5912   NaN
5913   NaN
5914   NaN
5915   NaN
5916   NaN
5917   NaN
5918   NaN
5919   NaN
5920   NaN
5921   NaN
5922   NaN
Name: avg_stand, dtype: float64
index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y   GYRO_X  GYRO_Y   GYRO_Z  state  stand
0          0  2016-03-26 13:49:04.72   -0.286    0.148    1.207  2016-03-26 13:49:05.06  -43.963  24.817   24.695      4      0
1          1  2016-03-26 13:49:04.72    0.030    0.050    1.042  2016-03-26 13:49:05.06   -0.427  27.073    8.171      4      0
2          2  2016-03-26 13:49:04.72   -0.064    0.104    0.998  2016-03-26 13:49:05.06   39.024  14.146  -45.976      4      0
3          3  2016-03-26 13:49:04.73    0.197    0.115    1.015  2016-03-26 13:49:05.07   14.878  -8.293  -59.939      4      0
4          4  2016-03-26 13:49:04.73    0.394   -0.045    0.931  2016-03-26 13:49:05.07   47.012 -47.439    0.671      4      0
5          5  2016-03-26 13:49:04.73    0.271   -0.245    1.017  2016-03-26 13:49:05.07   18.049 -15.549   18.780      4      0
6          6  2016-03-26 13:49:04.73    0.184   -0.200    1.009  2016-03-26 13:49:05.07  -35.305  -3.720  -34.329      4      0
7          7  2016-03-26 13:49:04.73    0.224    0.222    1.014  2016-03-26 13:49:05.07  -11.768  24.390  -13.049      4      0
8          8  2016-03-26 13:49:04.73    0.209   -0.111    1.047  2016-03-26 13:49:05.07    7.317  28.659   12.988      4      0
9          9  2016-03-26 13:49:04.73    0.209   -0.222    0.948  2016-03-26 13:49:05.07   19.695  15.732   21.463      4      0
10        10  2016-03-26 13:49:04.73    0.236   -0.008    1.187  2016-03-26 13:49:05.07  -16.159  48.963    4.634      4      0
11        11  2016-03-26 13:49:04.73    0.094    0.065    0.964  2016-03-26 13:49:05.07  -49.634  58.780  -46.098      4      0
12        12  2016-03-26 13:49:04.73    0.203   -0.042    0.964  2016-03-26 13:49:05.07   16.402   1.890  -27.134      4      0
13        13  2016-03-26 13:49:04.73    0.073    0.038    0.974  2016-03-26 13:49:05.07   -4.756  18.232  -17.988      4      0
14        14  2016-03-26 13:49:04.73   -0.007   -0.013    1.105  2016-03-26 13:49:05.07    1.829  29.817  -33.232      4      0
15        15  2016-03-26 13:49:04.73   -0.025   -0.394    1.003  2016-03-26 13:49:05.07  -16.463   1.280  -36.463      4      0
16        16  2016-03-26 13:49:04.73   -0.199   -0.176    1.014  2016-03-26 13:49:05.07  -14.390  16.524   -5.305      4      0
17        17  2016-03-26 13:49:04.73    0.073   -0.264    1.139  2016-03-26 13:49:05.07  -31.524  -0.671   13.110      4      0
18        18  2016-03-26 13:49:04.73    0.096   -0.100    0.917  2016-03-26 13:49:05.07  -33.598 -11.707   -1.220      4      0
19        19  2016-03-26 13:49:04.73    0.112   -0.087    1.052  2016-03-26 13:49:05.07  -18.293  -3.415  -26.280      4      0
20        20  2016-03-26 13:49:04.73    0.128   -0.013    1.023  2016-03-26 13:49:05.07   -0.915 -25.000   11.280      4      0
21        21  2016-03-26 13:49:04.73    0.151   -0.000    0.997  2016-03-26 13:49:05.07   11.220   2.073   23.902      4      0
22        22  2016-03-26 13:49:04.73    0.194   -0.092    0.980  2016-03-26 13:49:05.07   -3.598  20.183    5.122      4      0
23        23  2016-03-26 13:49:04.73    0.170    0.053    1.038  2016-03-26 13:49:05.07    1.585  22.805    7.378      4      0
24        24  2016-03-26 13:49:04.73    0.019    0.102    1.091  2016-03-26 13:49:05.07  -10.000  12.439    6.585      4      0
25        25  2016-03-26 13:49:04.73    0.109   -0.160    1.025  2016-03-26 13:49:05.07  -16.037  10.671    0.854      4      0
26        26  2016-03-26 13:49:04.73    0.092   -0.255    1.066  2016-03-26 13:49:05.07  -13.293  15.854    8.049      4      0
27        27  2016-03-26 13:49:04.74    0.059   -0.194    1.013  2016-03-26 13:49:05.07  -24.878  20.366   -1.159      4      0
28        28  2016-03-26 13:49:04.74   -0.247   -0.220    1.000  2016-03-26 13:49:05.07  -23.537   8.415  -14.329      4      0
29        29  2016-03-26 13:49:04.74   -0.294   -0.315    1.013  2016-03-26 13:49:05.07  -17.256  25.000  -13.354      4      0
...      ...                     ...      ...      ...      ...                     ...      ...     ...      ...    ...    ...
18552   2582  2016-03-12 13:59:17.01    0.269    0.005    1.034  2016-03-12 13:59:17.64  -57.683  79.268  -94.939      4      0
18553   2583  2016-03-12 13:59:17.01    0.279    0.020    0.969  2016-03-12 13:59:17.64  -43.476  59.329  -88.720      4      0
18554   2584  2016-03-12 13:59:17.01    0.330    0.093    0.939  2016-03-12 13:59:17.64  -18.780  62.866 -103.476      4      0
18555   2585  2016-03-12 13:59:17.01    0.169    0.123    0.999  2016-03-12 13:59:17.64  -18.780  62.500  -71.159      4      0
18556   2586  2016-03-12 13:59:17.01   -0.052    0.320    1.017  2016-03-12 13:59:17.64   28.293  33.659  -49.512      4      0
18557   2587  2016-03-12 13:59:17.01    0.029    0.016    0.957  2016-03-12 13:59:17.64   70.549   0.976  -28.232      4      0
18558   2588  2016-03-12 13:59:17.01    0.065   -0.285    1.114  2016-03-12 13:59:17.64   79.573  -6.037  -18.232      4      0
18559   2589  2016-03-12 13:59:17.01    0.004   -0.095    1.012  2016-03-12 13:59:17.64   95.244 -39.512    7.988      4      0
18560   2590  2016-03-12 13:59:17.01    0.063    0.133    0.945  2016-03-12 13:59:17.64  130.549 -55.732   31.402      4      0
18561   2591  2016-03-12 13:59:17.01    0.085   -0.396    1.133  2016-03-12 13:59:17.64  112.256 -50.671   47.195      4      0
18562   2592  2016-03-12 13:59:17.01    0.138   -0.209    1.140  2016-03-12 13:59:17.64   83.902 -64.817   68.659      4      0
18563   2593  2016-03-12 13:59:17.01    0.236    0.049    1.150  2016-03-12 13:59:17.64  173.110 -54.451   37.317      4      0
18564   2594  2016-03-12 13:59:17.01    0.131    0.017    1.076  2016-03-12 13:59:17.64  -19.207   5.366   44.390      4      0
18565   2595  2016-03-12 13:59:17.01   -0.059   -0.196    1.058  2016-03-12 13:59:17.64 -120.610 -28.841   60.061      4      0
18566   2596  2016-03-12 13:59:17.01   -0.092   -0.721    0.803                     NaN      NaN     NaN      NaN      4      0
18567   2597  2016-03-12 13:59:17.01   -0.131   -0.611    0.704                     NaN      NaN     NaN      NaN      4      0
18568   2598  2016-03-12 13:59:17.01   -0.190   -0.570    0.801                     NaN      NaN     NaN      NaN      4      0
18569   2599  2016-03-12 13:59:17.01   -0.066   -0.625    0.833                     NaN      NaN     NaN      NaN      4      0
18570   2600  2016-03-12 13:59:17.01   -0.067   -0.542    0.854                     NaN      NaN     NaN      NaN      4      0
18571   2601  2016-03-12 13:59:17.01    0.014   -0.411    1.007                     NaN      NaN     NaN      NaN      4      0
18572   2602  2016-03-12 13:59:17.01    0.155   -0.530    0.850                     NaN      NaN     NaN      NaN      4      0
18573   2603  2016-03-12 13:59:17.01    0.093   -0.590    0.808                     NaN      NaN     NaN      NaN      4      0
18574   2604  2016-03-12 13:59:17.01    0.007   -0.583    0.717                     NaN      NaN     NaN      NaN      4      0
18575   2605  2016-03-12 13:59:17.01    0.100   -0.448    0.848                     NaN      NaN     NaN      NaN      4      0
18576   2606  2016-03-12 13:59:17.01    0.048   -0.244    1.048                     NaN      NaN     NaN      NaN      4      0
18577   2607  2016-03-12 13:59:17.01   -0.027    0.023    1.019                     NaN      NaN     NaN      NaN      4      0
18578   2608  2016-03-12 13:59:17.01    0.079    0.505    1.218                     NaN      NaN     NaN      NaN      4      0
18579   2609  2016-03-12 13:59:17.01    0.371    0.384    1.313                     NaN      NaN     NaN      NaN      4      0
18580   2610  2016-03-12 13:59:17.01   -0.268   -0.076    0.845                     NaN      NaN     NaN      NaN      4      0
18581   2611  2016-03-12 13:59:17.01    0.136    0.014    1.010                     NaN      NaN     NaN      NaN      4      0

[18582 rows x 11 columns]
0       NaN
1       NaN
2       NaN
3       NaN
4       NaN
5       NaN
6       NaN
7       NaN
8       NaN
9       NaN
10      NaN
11      NaN
12      NaN
13      NaN
14      NaN
15      NaN
16      NaN
17      NaN
18      NaN
19      NaN
20      NaN
21      NaN
22      NaN
23      NaN
24        0
25        0
26        0
27        0
28        0
29        0
..
18552     0
18553     0
18554     0
18555     0
18556     0
18557     0
18558     0
18559   NaN
18560   NaN
18561   NaN
18562   NaN
18563   NaN
18564   NaN
18565   NaN
18566   NaN
18567   NaN
18568   NaN
18569   NaN
18570   NaN
18571   NaN
18572   NaN
18573   NaN
18574   NaN
18575   NaN
18576   NaN
18577   NaN
18578   NaN
18579   NaN
18580   NaN
18581   NaN
Name: avg_stand, dtype: float64
index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y   GYRO_X   GYRO_Y   GYRO_Z  state  stand
0          0  2016-03-26 13:39:22.18   -0.441    0.058   -1.054  2016-03-26 13:39:22.48   55.061   58.963   49.695      5      0
1          1  2016-03-26 13:39:22.18   -0.436    0.168   -1.169  2016-03-26 13:39:22.48   45.000   50.000   54.390      5      0
2          2  2016-03-26 13:39:22.18   -0.359    0.118   -1.037  2016-03-26 13:39:22.48  -23.598    6.341   63.780      5      0
3          3  2016-03-26 13:39:22.18   -0.392    0.296   -0.719  2016-03-26 13:39:22.48  -46.585    3.415   40.549      5      0
4          4  2016-03-26 13:39:22.18   -0.697    0.655   -0.847  2016-03-26 13:39:22.48  -41.951    8.902   28.232      5      0
5          5  2016-03-26 13:39:22.18   -0.918    0.820   -0.598  2016-03-26 13:39:22.48  -38.598   22.134   41.463      5      0
6          6  2016-03-26 13:39:22.18   -0.405    0.720   -0.682  2016-03-26 13:39:22.48  -93.293  -34.878   44.268      5      0
7          7  2016-03-26 13:39:22.18   -0.141    0.349   -0.785  2016-03-26 13:39:22.48  -95.610  -54.573   33.232      5      0
8          8  2016-03-26 13:39:22.18   -0.316    0.311   -0.680  2016-03-26 13:39:22.48 -115.610  -93.963   58.963      5      0
9          9  2016-03-26 13:39:22.19   -0.355    0.533   -0.922  2016-03-26 13:39:22.48  -55.915  -77.927   57.805      5      0
10        10  2016-03-26 13:39:22.19    0.428    0.964   -2.183  2016-03-26 13:39:22.48  -13.354  -61.707   35.854      5      0
11        11  2016-03-26 13:39:22.19   -1.301   -0.404    1.276  2016-03-26 13:39:22.48 -123.537  -87.439  -16.890      5      0
12        12  2016-03-26 13:39:22.19   -1.815    0.791   -0.550  2016-03-26 13:39:22.48   31.951  -79.756  -43.354      5      0
13        13  2016-03-26 13:39:22.19   -0.675    0.538   -0.846  2016-03-26 13:39:22.48   40.549   38.293  -12.500      5      0
14        14  2016-03-26 13:39:22.19    1.117    1.008   -1.513  2016-03-26 13:39:22.48  -57.073   89.756  -18.110      5      0
15        15  2016-03-26 13:39:22.19   -0.986    0.544   -0.327  2016-03-26 13:39:22.48 -186.768 -145.000 -104.756      5      0
16        16  2016-03-26 13:39:22.19   -0.834    0.356   -0.556  2016-03-26 13:39:22.48  -79.512   -5.793   65.244      5      0
17        17  2016-03-26 13:39:22.19   -0.179    0.759   -0.737  2016-03-26 13:39:22.48  -26.402  210.305 -123.720      5      0
18        18  2016-03-26 13:39:22.19   -0.544    0.323   -0.501  2016-03-26 13:39:22.48  108.963  116.646 -126.768      5      0
19        19  2016-03-26 13:39:22.19   -1.337    0.507   -0.823  2016-03-26 13:39:22.48   26.707  -79.695  -22.683      5      0
20        20  2016-03-26 13:39:22.19   -0.429    0.460   -0.447  2016-03-26 13:39:22.48   45.854  353.598   23.354      5      0
21        21  2016-03-26 13:39:22.19   -0.673   -0.260   -0.688  2016-03-26 13:39:22.48 -155.610  -85.244  -57.744      5      0
22        22  2016-03-26 13:39:22.19   -0.881   -0.011   -1.029  2016-03-26 13:39:22.48   -9.390 -264.695 -180.488      5      0
23        23  2016-03-26 13:39:22.19    0.700   -0.669   -1.198  2016-03-26 13:39:22.48   96.768    4.451  -90.000      5      0
24        24  2016-03-26 13:39:22.19   -0.831    0.989   -0.370  2016-03-26 13:39:22.48 -113.476 -106.341   43.963      5      0
25        25  2016-03-26 13:39:22.19   -0.780   -0.050   -0.919  2016-03-26 13:39:22.48   18.232  -90.976   31.646      5      0
26        26  2016-03-26 13:39:22.19   -0.430    0.120   -0.779  2016-03-26 13:39:22.48  109.939   17.256   69.024      5      0
27        27  2016-03-26 13:39:22.19   -0.456    0.102   -0.603  2016-03-26 13:39:22.48    0.549  140.793  -48.110      5      0
28        28  2016-03-26 13:39:22.19   -0.817    0.277   -0.914  2016-03-26 13:39:22.48   87.744  235.793  -93.293      5      0
29        29  2016-03-26 13:39:22.19   -1.025    0.438   -1.483  2016-03-26 13:39:22.48 -124.695  237.927  -44.939      5      0
...      ...                     ...      ...      ...      ...                     ...      ...      ...      ...    ...    ...
16730   2569  2016-03-12 14:10:05.83   -0.757    0.241   -0.563  2016-03-12 14:10:06.44   -0.732  -38.415  -14.451      5      0
16731   2570  2016-03-12 14:10:05.84   -0.675    0.287   -0.603  2016-03-12 14:10:06.44    7.012  -27.012  -15.183      5      0
16732   2571  2016-03-12 14:10:05.84   -0.711    0.262   -0.631  2016-03-12 14:10:06.44   -1.098  -21.280  -15.000      5      0
16733   2572  2016-03-12 14:10:05.84   -0.749    0.299   -0.678  2016-03-12 14:10:06.44   -9.451   -4.268  -17.073      5      0
16734   2573  2016-03-12 14:10:05.84   -0.863    0.396   -0.614  2016-03-12 14:10:06.44   -3.232   19.085   -3.963      5      0
16735   2574  2016-03-12 14:10:05.84   -0.944    0.392   -0.433  2016-03-12 14:10:06.44    6.646   21.829   -5.610      5      0
16736   2575  2016-03-12 14:10:05.84   -0.926    0.410   -0.413  2016-03-12 14:10:06.44    8.110   26.707   -4.390      5      0
16737   2576  2016-03-12 14:10:05.84   -0.912    0.358   -0.323  2016-03-12 14:10:06.44    9.878   30.854   -7.927      5      0
16738   2577  2016-03-12 14:10:05.84   -0.924    0.335   -0.232  2016-03-12 14:10:06.44    7.134   33.171   -7.073      5      0
16739   2578  2016-03-12 14:10:05.84   -0.929    0.362   -0.156  2016-03-12 14:10:06.44   -3.171   35.305   -9.207      5      0
16740   2579  2016-03-12 14:10:05.84   -1.003    0.291   -0.120  2016-03-12 14:10:06.44    2.256   28.537  -10.244      5      0
16741   2580  2016-03-12 14:10:05.84   -1.014    0.338    0.013  2016-03-12 14:10:06.44    0.854   18.659   -8.963      5      0
16742   2581  2016-03-12 14:10:05.84   -0.926    0.288   -0.032  2016-03-12 14:10:06.44    5.000   26.402   -9.451      5      0
16743   2582  2016-03-12 14:10:05.84   -0.862    0.340   -0.011  2016-03-12 14:10:06.44   -1.707   29.634   -7.988      5      0
16744   2583  2016-03-12 14:10:05.84   -0.870    0.312   -0.019  2016-03-12 14:10:06.44  -12.988   34.451  -14.024      5      0
16745   2584  2016-03-12 14:10:05.84   -0.939    0.367    0.005  2016-03-12 14:10:06.44  -24.573   41.402  -10.366      5      0
16746   2585  2016-03-12 14:10:05.84   -0.994    0.168   -0.001  2016-03-12 14:10:06.44  -34.939   35.427   -7.378      5      0
16747   2586  2016-03-12 14:10:05.84   -0.956    0.228   -0.020  2016-03-12 14:10:06.44  -45.122   34.085   -5.549      5      0
16748   2587  2016-03-12 14:10:05.84   -0.924    0.244   -0.092                     NaN      NaN      NaN      NaN      5      0
16749   2588  2016-03-12 14:10:05.84   -0.938    0.298   -0.162                     NaN      NaN      NaN      NaN      5      0
16750   2589  2016-03-12 14:10:05.84   -1.009    0.343   -0.213                     NaN      NaN      NaN      NaN      5      0
16751   2590  2016-03-12 14:10:05.84   -1.004    0.264   -0.246                     NaN      NaN      NaN      NaN      5      0
16752   2591  2016-03-12 14:10:05.84   -1.003    0.151   -0.294                     NaN      NaN      NaN      NaN      5      0
16753   2592  2016-03-12 14:10:05.84   -0.957    0.208   -0.311                     NaN      NaN      NaN      NaN      5      0
16754   2593  2016-03-12 14:10:05.84   -0.875    0.322   -0.258                     NaN      NaN      NaN      NaN      5      0
16755   2594  2016-03-12 14:10:05.84   -0.851    0.334   -0.294                     NaN      NaN      NaN      NaN      5      0
16756   2595  2016-03-12 14:10:05.84   -0.983    0.095   -0.325                     NaN      NaN      NaN      NaN      5      0
16757   2596  2016-03-12 14:10:05.84   -0.965    0.248   -0.365                     NaN      NaN      NaN      NaN      5      0
16758   2597  2016-03-12 14:10:05.84   -0.951    0.254   -0.402                     NaN      NaN      NaN      NaN      5      0
16759   2598  2016-03-12 14:10:05.84   -0.887    0.090   -0.452                     NaN      NaN      NaN      NaN      5      0

[16760 rows x 11 columns]
0       NaN
1       NaN
2       NaN
3       NaN
4       NaN
5       NaN
6       NaN
7       NaN
8       NaN
9       NaN
10      NaN
11      NaN
12      NaN
13      NaN
14      NaN
15      NaN
16      NaN
17      NaN
18      NaN
19      NaN
20      NaN
21      NaN
22      NaN
23      NaN
24        0
25        0
26        0
27        0
28        0
29        0
..
16730     0
16731     0
16732     0
16733     0
16734     0
16735     0
16736     0
16737   NaN
16738   NaN
16739   NaN
16740   NaN
16741   NaN
16742   NaN
16743   NaN
16744   NaN
16745   NaN
16746   NaN
16747   NaN
16748   NaN
16749   NaN
16750   NaN
16751   NaN
16752   NaN
16753   NaN
16754   NaN
16755   NaN
16756   NaN
16757   NaN
16758   NaN
16759   NaN
Name: avg_stand, dtype: float64
index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y  GYRO_X  GYRO_Y  GYRO_Z  state  stand
0         0  2016-03-26 13:42:56.89   -0.817    0.231    0.542  2016-03-26 13:42:57.22  12.439  16.768   6.890      6      0
1         1  2016-03-26 13:42:56.89   -0.840    0.246    0.551  2016-03-26 13:42:57.22  13.293  15.976   6.524      6      0
2         2  2016-03-26 13:42:56.89   -0.854    0.259    0.579  2016-03-26 13:42:57.22  15.549  15.671   6.585      6      0
3         3  2016-03-26 13:42:56.89   -0.845    0.251    0.588  2016-03-26 13:42:57.22  13.659  16.707   7.866      6      0
4         4  2016-03-26 13:42:56.89   -0.833    0.250    0.602  2016-03-26 13:42:57.22  11.037   4.268   7.012      6      0
5         5  2016-03-26 13:42:56.89   -0.832    0.267    0.616  2016-03-26 13:42:57.22   7.988   3.659   6.159      6      0
6         6  2016-03-26 13:42:56.89   -0.819    0.240    0.598  2016-03-26 13:42:57.22   5.000   0.305   6.098      6      0
7         7  2016-03-26 13:42:56.89   -0.805    0.224    0.579  2016-03-26 13:42:57.22   4.268  -5.610   4.634      6      0
8         8  2016-03-26 13:42:56.89   -0.777    0.240    0.614  2016-03-26 13:42:57.22  -0.976  -8.476   2.500      6      0
9         9  2016-03-26 13:42:56.89   -0.776    0.236    0.630  2016-03-26 13:42:57.22 -10.732 -19.329   0.427      6      0
10       10  2016-03-26 13:42:56.89   -0.766    0.196    0.668  2016-03-26 13:42:57.22 -17.439 -30.427  -0.427      6      0
11       11  2016-03-26 13:42:56.89   -0.777    0.210    0.708  2016-03-26 13:42:57.22 -18.293 -29.573  -1.463      6      0
12       12  2016-03-26 13:42:56.89   -0.745    0.264    0.735  2016-03-26 13:42:57.22 -11.341 -24.634   0.061      6      0
13       13  2016-03-26 13:42:56.89   -0.786    0.206    0.664  2016-03-26 13:42:57.22  -9.146 -20.122   2.988      6      0
14       14  2016-03-26 13:42:56.89   -0.694    0.172    0.681  2016-03-26 13:42:57.22  -9.207 -17.744   2.866      6      0
15       15  2016-03-26 13:42:56.89   -0.710    0.237    0.584  2016-03-26 13:42:57.22  -1.890 -12.805   3.232      6      0
16       16  2016-03-26 13:42:56.89   -0.731    0.349    0.559  2016-03-26 13:42:57.22  -2.500 -17.683  -0.122      6      0
17       17  2016-03-26 13:42:56.89   -0.760    0.329    0.676  2016-03-26 13:42:57.22 -13.049 -23.232  -8.232      6      0
18       18  2016-03-26 13:42:56.89   -0.902    0.276    0.726  2016-03-26 13:42:57.22 -17.622 -37.439  -1.890      6      0
19       19  2016-03-26 13:42:56.89   -0.956    0.082    0.713  2016-03-26 13:42:57.22 -17.378 -32.012  -8.780      6      0
20       20  2016-03-26 13:42:56.89   -0.891    0.033    0.629  2016-03-26 13:42:57.22  16.220  16.890   4.207      6      0
21       21  2016-03-26 13:42:56.89   -0.711    0.222    0.602  2016-03-26 13:42:57.22  17.927  16.402  14.390      6      0
22       22  2016-03-26 13:42:56.89   -0.697    0.287    0.564  2016-03-26 13:42:57.22  -8.476 -18.049  27.561      6      0
23       23  2016-03-26 13:42:56.90   -0.665    0.122    0.611  2016-03-26 13:42:57.22  -3.963  -2.317  22.683      6      0
24       24  2016-03-26 13:42:56.90   -0.662    0.111    0.743  2016-03-26 13:42:57.22  12.866  10.000  15.732      6      0
25       25  2016-03-26 13:42:56.90   -0.646    0.287    0.788  2016-03-26 13:42:57.23  17.561  -1.463   7.927      6      0
26       26  2016-03-26 13:42:56.90   -0.540    0.152    0.803  2016-03-26 13:42:57.23   4.451 -39.939  12.134      6      0
27       27  2016-03-26 13:42:56.90   -0.544    0.144    0.807  2016-03-26 13:42:57.23   7.439 -74.024  31.890      6      0
28       28  2016-03-26 13:42:56.90   -0.426    0.275    0.817  2016-03-26 13:42:57.23   3.780 -79.146  40.427      6      0
29       29  2016-03-26 13:42:56.90   -0.292    0.555    0.867  2016-03-26 13:42:57.23  14.451 -84.634  48.537      6      0
...     ...                     ...      ...      ...      ...                     ...     ...     ...     ...    ...    ...
8690   1414  2016-03-12 13:27:53.26   -0.932    0.406    0.200  2016-03-12 13:27:53.59  -2.195 -18.659  18.780      6      0
8691   1415  2016-03-12 13:27:53.26   -0.915    0.385    0.187  2016-03-12 13:27:53.59  -3.293 -18.780  15.854      6      0
8692   1416  2016-03-12 13:27:53.26   -0.872    0.396    0.236  2016-03-12 13:27:53.59  -3.963 -20.305   5.854      6      0
8693   1417  2016-03-12 13:27:53.26   -0.906    0.392    0.255  2016-03-12 13:27:53.59  -7.805  -6.951   3.659      6      0
8694   1418  2016-03-12 13:27:53.26   -0.872    0.407    0.289  2016-03-12 13:27:53.59  -3.963   3.598   2.805      6      0
8695   1419  2016-03-12 13:27:53.26   -0.915    0.386    0.278  2016-03-12 13:27:53.59  -9.451   5.732  -6.829      6      0
8696   1420  2016-03-12 13:27:53.26   -0.955    0.408    0.290  2016-03-12 13:27:53.59 -18.293  -4.756 -11.585      6      0
8697   1421  2016-03-12 13:27:53.26   -0.939    0.376    0.261  2016-03-12 13:27:53.59 -19.939  -7.317 -12.378      6      0
8698   1422  2016-03-12 13:27:53.26   -0.901    0.364    0.280  2016-03-12 13:27:53.59 -13.476  -4.756 -11.159      6      0
8699   1423  2016-03-12 13:27:53.26   -0.849    0.322    0.313  2016-03-12 13:27:53.59  -7.805   7.012  -8.049      6      0
8700   1424  2016-03-12 13:27:53.26   -0.963    0.333    0.325  2016-03-12 13:27:53.60  -5.854   3.110  -9.451      6      0
8701   1425  2016-03-12 13:27:53.26   -0.953    0.313    0.292  2016-03-12 13:27:53.60  -5.305  -1.220 -10.061      6      0
8702   1426  2016-03-12 13:27:53.26   -0.932    0.286    0.282  2016-03-12 13:27:53.60  -3.841  -1.463  -8.537      6      0
8703   1427  2016-03-12 13:27:53.26   -0.891    0.265    0.290  2016-03-12 13:27:53.60  -2.256   1.159  -6.159      6      0
8704   1428  2016-03-12 13:27:53.26   -0.972    0.276    0.324  2016-03-12 13:27:53.60  -5.488  -4.329  -5.793      6      0
8705   1429  2016-03-12 13:27:53.26   -0.943    0.269    0.289  2016-03-12 13:27:53.60  -5.305  -3.659  -5.061      6      0
8706   1430  2016-03-12 13:27:53.26   -0.937    0.257    0.306  2016-03-12 13:27:53.60  -5.244  -4.878  -5.061      6      0
8707   1431  2016-03-12 13:27:53.26   -0.913    0.272    0.299  2016-03-12 13:27:53.60  -0.244   0.183  -7.256      6      0
8708   1432  2016-03-12 13:27:53.26   -0.943    0.266    0.309  2016-03-12 13:27:53.60   3.598   2.988  -9.451      6      0
8709   1433  2016-03-12 13:27:53.26   -0.929    0.251    0.335  2016-03-12 13:27:53.60   0.549   0.854  -4.512      6      0
8710   1434  2016-03-12 13:27:53.26   -0.903    0.273    0.298  2016-03-12 13:27:53.60  -4.451   8.598   0.000      6      0
8711   1435  2016-03-12 13:27:53.26   -0.917    0.261    0.292  2016-03-12 13:27:53.60  -3.659   9.085   0.793      6      0
8712   1436  2016-03-12 13:27:53.26   -0.935    0.236    0.298  2016-03-12 13:27:53.60  -5.427   9.695   0.732      6      0
8713   1437  2016-03-12 13:27:53.26   -0.945    0.257    0.271  2016-03-12 13:27:53.60  -3.110  11.159   1.402      6      0
8714   1438  2016-03-12 13:27:53.26   -0.957    0.253    0.280  2016-03-12 13:27:53.60  -1.585  10.366   0.000      6      0
8715   1439  2016-03-12 13:27:53.26   -0.956    0.254    0.290  2016-03-12 13:27:53.60  -1.402   5.915  -1.463      6      0
8716   1440  2016-03-12 13:27:53.26   -0.956    0.254    0.273  2016-03-12 13:27:53.60  -1.220   5.122  -0.671      6      0
8717   1441  2016-03-12 13:27:53.26   -0.960    0.245    0.270  2016-03-12 13:27:53.60  -0.427   5.183  -1.159      6      0
8718   1442  2016-03-12 13:27:53.26   -0.961    0.250    0.274  2016-03-12 13:27:53.60  -0.488   0.915  -1.768      6      0
8719   1443  2016-03-12 13:27:53.26   -0.954    0.249    0.266                     NaN     NaN     NaN     NaN      6      0

[8720 rows x 11 columns]
0      NaN
1      NaN
2      NaN
3      NaN
4      NaN
5      NaN
6      NaN
7      NaN
8      NaN
9      NaN
10     NaN
11     NaN
12     NaN
13     NaN
14     NaN
15     NaN
16     NaN
17     NaN
18     NaN
19     NaN
20     NaN
21     NaN
22     NaN
23     NaN
24       0
25       0
26       0
27       0
28       0
29       0
..
8690     0
8691     0
8692     0
8693     0
8694     0
8695     0
8696     0
8697   NaN
8698   NaN
8699   NaN
8700   NaN
8701   NaN
8702   NaN
8703   NaN
8704   NaN
8705   NaN
8706   NaN
8707   NaN
8708   NaN
8709   NaN
8710   NaN
8711   NaN
8712   NaN
8713   NaN
8714   NaN
8715   NaN
8716   NaN
8717   NaN
8718   NaN
8719   NaN
Name: avg_stand, dtype: float64
index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y  GYRO_X  GYRO_Y  GYRO_Z  state  stand
0          0  2016-03-26 14:12:39.00   -0.800    0.588    0.113  2016-03-26 14:12:40.64   1.402  -4.268   1.951      7      0
1          1  2016-03-26 14:12:39.00   -0.816    0.581    0.151  2016-03-26 14:12:40.64   1.707  -4.146   2.195      7      0
2          2  2016-03-26 14:12:39.00   -0.809    0.599    0.173  2016-03-26 14:12:40.64  -0.244  -9.268   4.390      7      0
3          3  2016-03-26 14:12:40.00   -0.823    0.597    0.165  2016-03-26 14:12:40.64  -3.049 -11.585   6.037      7      0
4          4  2016-03-26 14:12:40.00   -0.818    0.574    0.183  2016-03-26 14:12:40.64   0.244  -9.512   4.756      7      0
5          5  2016-03-26 14:12:40.00   -0.817    0.594    0.173  2016-03-26 14:12:40.64  -0.427  -6.951   2.622      7      0
6          6  2016-03-26 14:12:40.00   -0.820    0.582    0.165  2016-03-26 14:12:40.64  -1.829  -5.488   1.524      7      0
7          7  2016-03-26 14:12:40.00   -0.833    0.600    0.155  2016-03-26 14:12:40.64  -2.988  -7.927   0.061      7      0
8          8  2016-03-26 14:12:40.00   -0.837    0.609    0.155  2016-03-26 14:12:40.64  -1.585  -3.659  -2.683      7      0
9          9  2016-03-26 14:12:40.00   -0.831    0.582    0.149  2016-03-26 14:12:40.64  -1.951  -2.500  -4.085      7      0
10        10  2016-03-26 14:12:40.00   -0.823    0.590    0.143  2016-03-26 14:12:40.64  -0.976  -2.073  -4.085      7      0
11        11  2016-03-26 14:12:40.00   -0.832    0.582    0.147  2016-03-26 14:12:40.64  -0.061  -0.732  -2.744      7      0
12        12  2016-03-26 14:12:40.00   -0.849    0.588    0.141  2016-03-26 14:12:40.64   0.122  -2.256  -2.561      7      0
13        13  2016-03-26 14:12:40.00   -0.833    0.591    0.127  2016-03-26 14:12:40.64   3.049   0.183  -0.183      7      0
14        14  2016-03-26 14:12:40.00   -0.817    0.591    0.120  2016-03-26 14:12:40.64   3.354  -0.488   0.854      7      0
15        15  2016-03-26 14:12:40.00   -0.832    0.590    0.128  2016-03-26 14:12:40.64   1.037  -1.341  -0.427      7      0
16        16  2016-03-26 14:12:40.01   -0.829    0.604    0.118  2016-03-26 14:12:40.64  -0.183  -3.537   0.061      7      0
17        17  2016-03-26 14:12:40.01   -0.819    0.568    0.152  2016-03-26 14:12:40.64  -0.122  -3.720   2.378      7      0
18        18  2016-03-26 14:12:40.01   -0.833    0.559    0.173  2016-03-26 14:12:40.64  -0.427   0.000   1.951      7      0
19        19  2016-03-26 14:12:40.01   -0.842    0.592    0.184  2016-03-26 14:12:40.64  -1.524   2.805   0.610      7      0
20        20  2016-03-26 14:12:40.01   -0.850    0.571    0.204  2016-03-26 14:12:40.64  -4.207   2.378  -0.854      7      0
21        21  2016-03-26 14:12:40.01   -0.825    0.546    0.212  2016-03-26 14:12:40.64  -3.902   1.890   1.341      7      0
22        22  2016-03-26 14:12:40.01   -0.803    0.547    0.206  2016-03-26 14:12:40.64  -4.024  -2.805   0.976      7      0
23        23  2016-03-26 14:12:40.01   -0.806    0.596    0.209  2016-03-26 14:12:40.64  -5.732  -7.317  -0.732      7      0
24        24  2016-03-26 14:12:40.01   -0.805    0.597    0.223  2016-03-26 14:12:40.64  -5.610  -5.854  -1.951      7      0
25        25  2016-03-26 14:12:40.01   -0.830    0.559    0.223  2016-03-26 14:12:40.65  -0.549  -8.293  -1.524      7      0
26        26  2016-03-26 14:12:40.01   -0.842    0.570    0.199  2016-03-26 14:12:40.65   7.683  -9.024   0.244      7      0
27        27  2016-03-26 14:12:40.01   -0.830    0.586    0.165  2016-03-26 14:12:40.65   7.500  -5.366   1.463      7      0
28        28  2016-03-26 14:12:40.01   -0.827    0.579    0.173  2016-03-26 14:12:40.65   8.110   0.122   0.183      7      0
29        29  2016-03-26 14:12:40.01   -0.829    0.601    0.151  2016-03-26 14:12:40.65   6.829   1.463  -0.427      7      0
...      ...                     ...      ...      ...      ...                     ...     ...     ...     ...    ...    ...
48810   7665  2016-03-05 13:44:42.30   -0.831   -0.293    0.529  2016-03-05 13:44:44.03  -0.244   3.110  -0.366      7      0
48811   7666  2016-03-05 13:44:42.30   -0.833   -0.294    0.515  2016-03-05 13:44:44.03   0.671   2.927  -0.732      7      0
48812   7667  2016-03-05 13:44:42.30   -0.831   -0.291    0.519  2016-03-05 13:44:44.03   0.793   0.671  -0.732      7      0
48813   7668  2016-03-05 13:44:42.30   -0.836   -0.294    0.522  2016-03-05 13:44:44.03   1.098   1.159  -0.549      7      0
48814   7669  2016-03-05 13:44:42.30   -0.836   -0.293    0.519  2016-03-05 13:44:44.03  -1.524   2.256   0.183      7      0
48815   7670  2016-03-05 13:44:42.30   -0.832   -0.294    0.518  2016-03-05 13:44:44.03  -1.341   3.537   0.000      7      0
48816   7671  2016-03-05 13:44:42.30   -0.836   -0.297    0.517  2016-03-05 13:44:44.03  -1.220   2.073   0.000      7      0
48817   7672  2016-03-05 13:44:42.30   -0.838   -0.294    0.514  2016-03-05 13:44:44.03  -0.671   2.195   0.000      7      0
48818   7673  2016-03-05 13:44:42.30   -0.834   -0.294    0.512  2016-03-05 13:44:44.03  -0.610   1.037   0.122      7      0
48819   7674  2016-03-05 13:44:42.30   -0.835   -0.299    0.514  2016-03-05 13:44:44.03  -1.402   0.488   0.183      7      0
48820   7675  2016-03-05 13:44:42.30   -0.837   -0.297    0.513  2016-03-05 13:44:44.03  -0.854   1.463   0.244      7      0
48821   7676  2016-03-05 13:44:42.30   -0.840   -0.293    0.507  2016-03-05 13:44:44.03  -0.427   1.585   0.366      7      0
48822   7677  2016-03-05 13:44:42.30   -0.841   -0.295    0.509  2016-03-05 13:44:44.03  -1.037   1.463   0.610      7      0
48823   7678  2016-03-05 13:44:42.30   -0.841   -0.296    0.507  2016-03-05 13:44:44.03  -0.976   1.524   0.549      7      0
48824   7679  2016-03-05 13:44:42.30   -0.841   -0.299    0.505  2016-03-05 13:44:44.03  -0.793   1.220   0.427      7      0
48825   7680  2016-03-05 13:44:42.30   -0.843   -0.302    0.511  2016-03-05 13:44:44.03  -0.671   0.976   0.427      7      0
48826   7681  2016-03-05 13:44:42.30   -0.838   -0.295    0.521  2016-03-05 13:44:44.03  -0.244   1.220   0.427      7      0
48827   7682  2016-03-05 13:44:42.30   -0.835   -0.294    0.526  2016-03-05 13:44:44.03   0.000   1.646   0.488      7      0
48828   7683  2016-03-05 13:44:42.30   -0.829   -0.295    0.520  2016-03-05 13:44:44.03  -0.061   1.037   0.549      7      0
48829   7684  2016-03-05 13:44:42.30   -0.835   -0.298    0.519  2016-03-05 13:44:44.03   0.732   0.976   0.305      7      0
48830   7685  2016-03-05 13:44:42.30   -0.829   -0.292    0.527  2016-03-05 13:44:44.03   1.341  -3.720   0.061      7      0
48831   7686  2016-03-05 13:44:42.30   -0.829   -0.295    0.527  2016-03-05 13:44:44.03   3.354  -6.402   0.000      7      0
48832   7687  2016-03-05 13:44:42.30   -0.828   -0.293    0.529  2016-03-05 13:44:44.03   0.854  -7.073   1.280      7      0
48833   7688  2016-03-05 13:44:42.30   -0.821   -0.292    0.538  2016-03-05 13:44:44.03  -0.549  -4.268   1.220      7      0
48834   7689  2016-03-05 13:44:42.30   -0.820   -0.294    0.539  2016-03-05 13:44:44.03   0.610  -7.500   0.732      7      0
48835   7690  2016-03-05 13:44:42.30   -0.819   -0.295    0.546  2016-03-05 13:44:44.03   0.183  -7.256   0.732      7      0
48836   7691                     NaN      NaN      NaN      NaN  2016-03-05 13:44:44.03   0.854  -6.829   0.366      7      0
48837   7692                     NaN      NaN      NaN      NaN  2016-03-05 13:44:44.03   0.610  -7.866   0.183      7      0
48838   7693                     NaN      NaN      NaN      NaN  2016-03-05 13:44:44.03   0.305  -6.037   0.427      7      0
48839   7694                     NaN      NaN      NaN      NaN  2016-03-05 13:44:44.03   1.037  -5.122   0.488      7      0

[48840 rows x 11 columns]
0       NaN
1       NaN
2       NaN
3       NaN
4       NaN
5       NaN
6       NaN
7       NaN
8       NaN
9       NaN
10      NaN
11      NaN
12      NaN
13      NaN
14      NaN
15      NaN
16      NaN
17      NaN
18      NaN
19      NaN
20      NaN
21      NaN
22      NaN
23      NaN
24        0
25        0
26        0
27        0
28        0
29        0
..
48810     0
48811     0
48812     0
48813     0
48814     0
48815     0
48816     0
48817   NaN
48818   NaN
48819   NaN
48820   NaN
48821   NaN
48822   NaN
48823   NaN
48824   NaN
48825   NaN
48826   NaN
48827   NaN
48828   NaN
48829   NaN
48830   NaN
48831   NaN
48832   NaN
48833   NaN
48834   NaN
48835   NaN
48836   NaN
48837   NaN
48838   NaN
48839   NaN
Name: avg_stand, dtype: float64
index             timestamp_x  ACCEL_X  ACCEL_Y  ACCEL_Z             timestamp_y  GYRO_X   GYRO_Y  GYRO_Z  state  stand
0         0  2016-05-01 13:22:01.80   -1.060    0.169   -0.090  2016-05-01 13:22:01.84   6.829   26.829  -3.415      5      1
1         1  2016-05-01 13:22:01.80   -1.083    0.177   -0.104  2016-05-01 13:22:01.84   6.463   27.683  -3.171      5      1
2         2  2016-05-01 13:22:01.80   -1.098    0.178   -0.128  2016-05-01 13:22:01.84   2.683   32.866  -3.171      5      1
3         3  2016-05-01 13:22:01.80   -1.083    0.197   -0.138  2016-05-01 13:22:01.84   6.159   27.622  -1.524      5      1
4         4  2016-05-01 13:22:01.80   -1.068    0.176   -0.114  2016-05-01 13:22:01.84   8.110   24.268   0.793      5      1
5         5  2016-05-01 13:22:01.80   -1.078    0.176   -0.131  2016-05-01 13:22:01.84   4.451   26.098  -0.061      5      1
6         6  2016-05-01 13:22:01.80   -1.071    0.190   -0.136  2016-05-01 13:22:01.84   2.012   19.817  -1.402      5      1
7         7  2016-05-01 13:22:01.80   -1.072    0.180   -0.130  2016-05-01 13:22:01.84   4.146   13.720  -1.220      5      1
8         8  2016-05-01 13:22:01.80   -1.072    0.194   -0.125  2016-05-01 13:22:01.84   3.902    9.329  -0.976      5      1
9         9  2016-05-01 13:22:01.81   -1.078    0.188   -0.115  2016-05-01 13:22:01.84   1.037    5.183   1.463      5      1
10       10  2016-05-01 13:22:01.81   -1.085    0.192   -0.092  2016-05-01 13:22:01.84   1.646    4.329   1.098      5      1
11       11  2016-05-01 13:22:01.81   -1.076    0.186   -0.087  2016-05-01 13:22:01.84   4.512    3.171   0.732      5      1
12       12  2016-05-01 13:22:01.81   -1.067    0.193   -0.090  2016-05-01 13:22:01.84   1.829    3.780   2.927      5      1
13       13  2016-05-01 13:22:01.81   -1.060    0.197   -0.084  2016-05-01 13:22:01.84   0.122    0.854   2.500      5      1
14       14  2016-05-01 13:22:01.81   -1.064    0.201   -0.080  2016-05-01 13:22:01.84   0.183   -1.037   1.890      5      1
15       15  2016-05-01 13:22:01.81   -1.078    0.203   -0.081  2016-05-01 13:22:01.84   0.427   -3.049   1.829      5      1
16       16  2016-05-01 13:22:01.81   -1.067    0.198   -0.079  2016-05-01 13:22:01.84   0.000   -5.610   1.646      5      1
17       17  2016-05-01 13:22:01.81   -1.064    0.195   -0.077  2016-05-01 13:22:01.84  -0.366   -5.549   0.976      5      1
18       18  2016-05-01 13:22:01.81   -1.067    0.195   -0.064  2016-05-01 13:22:01.84   0.000   -5.488   0.854      5      1
19       19  2016-05-01 13:22:01.81   -1.068    0.205   -0.069  2016-05-01 13:22:01.84   1.463   -3.902   0.671      5      1
20       20  2016-05-01 13:22:01.81   -1.069    0.198   -0.081  2016-05-01 13:22:01.84  -0.549   -3.720   0.488      5      1
21       21  2016-05-01 13:22:01.81   -1.080    0.190   -0.064  2016-05-01 13:22:01.84  -1.463   -5.183  -0.061      5      1
22       22  2016-05-01 13:22:01.81   -1.083    0.180   -0.054  2016-05-01 13:22:01.84  -2.927   -2.378  -0.915      5      1
23       23  2016-05-01 13:22:01.81   -1.068    0.191   -0.066  2016-05-01 13:22:01.84  -0.976   -4.329  -1.951      5      1
24       24  2016-05-01 13:22:01.81   -1.065    0.217   -0.073  2016-05-01 13:22:01.84  -1.585   -2.317  -2.622      5      1
25       25  2016-05-01 13:22:01.81   -1.000    0.185   -0.157  2016-05-01 13:22:01.84  -3.841   -0.305  -2.073      5      1
26       26  2016-05-01 13:22:01.81   -1.097   -0.023   -0.322  2016-05-01 13:22:01.84  -2.500   -1.402  -2.073      5      1
27       27  2016-05-01 13:22:01.81   -1.186   -0.012    0.354  2016-05-01 13:22:01.84  -1.098   -4.268  -2.134      5      1
28       28  2016-05-01 13:22:01.81   -1.170   -0.059    0.316  2016-05-01 13:22:01.84  -3.902   -6.037  -2.927      5      1
29       29  2016-05-01 13:22:01.81   -1.337   -0.103    0.068  2016-05-01 13:22:01.84  -5.305   -5.366  -3.110      5      1
...     ...                     ...      ...      ...      ...                     ...     ...      ...     ...    ...    ...
1710     63  2016-05-01 12:56:53.42   -0.854    0.235   -0.673  2016-05-01 12:56:53.44  57.256 -117.622 -56.524      5      1
1711     64  2016-05-01 12:56:53.42   -0.900    0.234   -0.680  2016-05-01 12:56:53.44 -47.256  -75.488 -74.268      5      1
1712     65  2016-05-01 12:56:53.42   -0.853    0.128   -0.632  2016-05-01 12:56:53.44 -60.549 -107.134 -78.110      5      1
1713     66  2016-05-01 12:56:53.42   -0.896    0.107   -0.721  2016-05-01 12:56:53.44  19.756 -111.951 -81.463      5      1
1714     67  2016-05-01 12:56:53.42   -0.924    0.028   -0.602  2016-05-01 12:56:53.44 -33.110   -1.890 -73.720      5      1
1715     68  2016-05-01 12:56:53.42   -0.886    0.037   -0.442  2016-05-01 12:56:53.44  21.341   30.244 -56.585      5      1
1716     69  2016-05-01 12:56:53.42   -0.830   -0.019   -0.582  2016-05-01 12:56:53.44  24.451  -74.634 -48.293      5      1
1717     70  2016-05-01 12:56:53.42   -0.919   -0.037   -0.599  2016-05-01 12:56:53.44  25.671  -56.220 -39.878      5      1
1718     71  2016-05-01 12:56:53.42   -0.915   -0.017   -0.408  2016-05-01 12:56:53.44  56.220  -62.805 -32.256      5      1
1719     72  2016-05-01 12:56:53.42   -0.949   -0.067    0.006  2016-05-01 12:56:53.44  47.805  -79.756 -24.878      5      1
1720     73  2016-05-01 12:56:53.42   -0.945   -0.083   -0.385  2016-05-01 12:56:53.44  46.585  -78.476 -16.159      5      1
1721     74  2016-05-01 12:56:53.42   -0.941   -0.080   -0.241  2016-05-01 12:56:53.44  68.720  -40.244  -9.024      5      1
1722     75  2016-05-01 12:56:53.42   -1.000   -0.102   -0.250  2016-05-01 12:56:53.44  24.268  -24.329  -4.390      5      1
1723     76  2016-05-01 12:56:53.42   -0.960    0.000   -0.143  2016-05-01 12:56:53.44  71.585  -19.939  -4.207      5      1
1724     77  2016-05-01 12:56:53.42   -0.983    0.009   -0.210  2016-05-01 12:56:53.44 -16.037  -60.244  10.854      5      1
1725     78  2016-05-01 12:56:53.42   -0.999   -0.030   -0.170  2016-05-01 12:56:53.44  15.732  -77.500  13.841      5      1
1726     79  2016-05-01 12:56:53.42   -1.020   -0.024   -0.122  2016-05-01 12:56:53.45  29.756  -58.659  14.207      5      1
1727     80  2016-05-01 12:56:53.42   -1.029   -0.008   -0.110  2016-05-01 12:56:53.45  44.085  -42.622  16.280      5      1
1728     81  2016-05-01 12:56:53.42   -1.023   -0.017   -0.062  2016-05-01 12:56:53.45  19.634  -51.341  17.073      5      1
1729     82  2016-05-01 12:56:53.42   -0.997   -0.009   -0.033  2016-05-01 12:56:53.45  22.683  -46.524  13.902      5      1
1730     83  2016-05-01 12:56:53.42   -0.970   -0.016    0.012  2016-05-01 12:56:53.45  13.049  -44.329   8.049      5      1
1731     84  2016-05-01 12:56:53.42   -0.930   -0.045   -0.046  2016-05-01 12:56:53.45   5.122  -42.195   5.671      5      1
1732     85  2016-05-01 12:56:53.42   -0.990   -0.034   -0.088  2016-05-01 12:56:53.45   3.232  -38.902   3.963      5      1
1733     86  2016-05-01 12:56:53.42   -1.017   -0.039   -0.029  2016-05-01 12:56:53.45   0.427  -31.829   0.244      5      1
1734     87  2016-05-01 12:56:53.42   -1.085   -0.061   -0.070  2016-05-01 12:56:53.45  -0.793  -25.732  -1.585      5      1
1735     88  2016-05-01 12:56:53.42   -1.195   -0.057   -0.067  2016-05-01 12:56:53.45  -4.329  -20.793  -2.073      5      1
1736     89                     NaN      NaN      NaN      NaN  2016-05-01 12:56:53.45  -5.000  -13.537  -0.793      5      1
1737     90                     NaN      NaN      NaN      NaN  2016-05-01 12:56:53.45  -5.671    0.061   2.073      5      1
1738     91                     NaN      NaN      NaN      NaN  2016-05-01 12:56:53.45  -3.902    2.439   4.024      5      1
1739     92                     NaN      NaN      NaN      NaN  2016-05-01 12:56:53.45  -5.305  -10.610   3.415      5      1

[1740 rows x 11 columns]
0      NaN
1      NaN
2      NaN
3      NaN
4      NaN
5      NaN
6      NaN
7      NaN
8      NaN
9      NaN
10     NaN
11     NaN
12     NaN
13     NaN
14     NaN
15     NaN
16     NaN
17     NaN
18     NaN
19     NaN
20     NaN
21     NaN
22     NaN
23     NaN
24       1
25       1
26       1
27       1
28       1
29       1
..
1710     1
1711     1
1712     1
1713     1
1714     1
1715     1
1716     1
1717   NaN
1718   NaN
1719   NaN
1720   NaN
1721   NaN
1722   NaN
1723   NaN
1724   NaN
1725   NaN
1726   NaN
1727   NaN
1728   NaN
1729   NaN
1730   NaN
1731   NaN
1732   NaN
1733   NaN
1734   NaN
1735   NaN
1736   NaN
1737   NaN
1738   NaN
1739   NaN
Name: avg_stand, dtype: float64
Removed 118 NaN rows

``````
``````

In [66]:

print training_data50.stand.describe()

``````
``````

count    5367
mean        0
std         0
min         0
25%         0
50%         0
75%         0
max         0
Name: stand, dtype: float64

``````
``````

In [200]:

training_data = prep(30)
training_data10 = prep(10)
training_data20 = prep(20)
training_data26 = prep(26) # need numbers that divide into 2 easily
training_data36 = prep(36) # need numbers that divide into 2 easily
training_data40 = prep(40)
#training_data50 = prep(50)
training_data56 = prep(56)
training_data60 = prep(60)
training_data64 = prep(64)
training_data70 = prep(70)

``````
``````

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

Removed 109 NaN rows
Removed 186 NaN rows
Removed 127 NaN rows
Removed 114 NaN rows
Removed 101 NaN rows
Removed 99 NaN rows
Removed 93 NaN rows

``````
``````

In [43]:

print training_data50[training_data50.avg_stand == 0]

``````
``````

index     tiltx     tilty  stand   ACCEL_X   ACCEL_Y   ACCEL_Z     GYRO_X     GYRO_Y     GYRO_Z  ...    max_min_gx  max_min_gy  max_min_gz   acc_rss   gyro_rss   acc_rms   gyro_rms  acc_magnitude  gyro_magnitude  state
0        24 -0.337718 -0.074182      0 -0.298854  0.061854 -0.905396  -3.197437  -8.581000   2.266271  ...       287.927     287.927     287.927  0.955448   9.433618  0.551628   5.446502      -1.142396       -9.512167      0
1        49  0.078648  0.178678      0 -0.249208  0.099646 -0.944208   8.991375  -3.570812   2.945875  ...       279.695     279.695     279.695  0.981613  10.113046  0.566734   5.838770      -1.093771        8.366438      0
2        74 -0.308385  0.009462      0 -0.186458  0.086958 -0.948500 -10.496646  -1.448104   0.388729  ...       295.853     295.853     295.853  0.970557  10.603192  0.560351   6.121756      -1.048000      -11.556021      0
3        99  0.442236  0.669933      0 -0.096833  0.180833 -0.947500  -4.448667   6.206813  14.322896  ...       146.768     146.768     146.768  0.969450  16.231466  0.559712   9.371241      -0.863500       16.081042      0
4       124 -0.005017  0.182633      0  0.024417  0.202688 -0.971354   5.721563   5.988229   4.546500  ...       243.170     243.170     243.170  0.992576   9.448059  0.573064   5.454840      -0.744250       16.256292      0
5       149 -0.758348  0.332613      0 -0.015021  0.189625 -0.946208   7.996771  -2.223042 -12.204042  ...       243.170     243.170     243.170  0.965139  14.759027  0.557223   8.521128      -0.771604       -6.430312      0
6       174 -0.053906  0.131380      0 -0.288563  0.125521 -0.898000   4.512208  -8.610167   2.524021  ...       215.487     215.487     215.487  0.951540  10.043190  0.549372   5.798439      -1.061042       -1.573937      0
7       199 -0.611937  0.220517      0 -0.301167  0.159813 -0.878583  -2.760437   1.610792  -0.980729  ...       280.610     280.610     280.610  0.942417   3.343126  0.544105   1.930155      -1.019938       -2.130375      0
8       224 -0.093318  0.131612      0 -0.102833  0.245688 -0.923479   6.840771   2.183646 -19.349562  ...       280.610     280.610     280.610  0.961120  20.639041  0.554903  11.915956      -0.780625      -10.325146      0
9       249 -0.482192  0.442244      0 -0.183625  0.285833 -0.888812   1.906792  -7.258688   2.567313  ...       192.195     192.195     192.195  0.951529   7.931929  0.549365   4.579501      -0.786604       -2.784583      0
10      274 -0.196102  0.662022      0 -0.342021  0.324292 -0.824250   4.170479   1.708625 -16.910604  ...       285.183     285.183     285.183  0.949490  17.500881  0.548188  10.104138      -0.841979      -11.031500      0
11      299 -0.659925  0.415087      0 -0.322667  0.174271 -0.859229   9.672292  16.950000 -16.819167  ...       361.585     361.585     361.585  0.934216  25.763154  0.539370  14.874364      -1.007625        9.803125      0
12      324  0.200988 -0.109372      0 -0.103562  0.065604 -0.925833   8.816000   0.779979   2.109979  ...       351.951     351.951     351.951  0.933915   9.098474  0.539196   5.253007      -0.963792       11.705958      0
13      349  0.151612  0.574594      0  0.014188  0.032292 -0.907562   6.408771   6.110229  -4.081521  ...       719.452     719.452     719.452  0.908248   9.750182  0.524377   5.629270      -0.861083        8.437479      0
14      374  0.431029 -0.317504      0  0.112375 -0.133146 -0.925687  -4.432104   6.739042   6.743000  ...       719.452     719.452     719.452  0.941941  10.513148  0.543830   6.069769      -0.946458        9.049938      0
15      399  0.264143  0.027012      0  0.195417 -0.078167 -0.938917  -4.456208 -13.741125   9.098167  ...       177.744     177.744     177.744  0.962217  17.071993  0.555536   9.856520      -0.821667       -9.099167      0
16      424 -0.146925  0.494388      0 -0.122958  0.060312 -0.918729  -2.116333 -26.361771  -0.782479  ...       233.659     233.659     233.659  0.928881  26.458158  0.536290  15.275624      -0.981375      -29.260583      0
17      449 -0.510741 -0.171934      0 -0.427542  0.007208 -0.910479   5.655417  18.906271  -6.754250  ...       233.598     233.598     233.598  1.005891  20.857869  0.580751  12.042296      -1.330812       17.807438      0
18      474 -0.910436  0.248410      0 -0.193208 -0.075667 -0.890437  -1.979187  25.706292   2.709667  ...       151.280     151.280     151.280  0.914294  25.924369  0.527868  14.967441      -1.159312       26.436771      0
19      499  0.012917  0.312465      0  0.087708 -0.027667 -0.985812 -17.568562  -9.383917  -2.719792  ...       168.964     168.964     168.964  0.990093  20.102476  0.571631  11.606170      -0.925771      -29.672271      0
20      524  0.109811 -0.398053      0  0.076063  0.206000 -0.906667 -10.887958  -9.507104   0.142167  ...       172.317     172.317     172.317  0.932880  14.455202  0.538599   8.345715      -0.624604      -20.252896      0
21      549  0.630280  0.265899      0 -0.107875  0.342604 -0.848437   3.593750 -27.590167   1.028833  ...       269.086     269.086     269.086  0.921336  27.842249  0.531934  16.074730      -0.613708      -22.967583      0
22      574 -0.925740  0.209614      0 -0.428667  0.195979 -0.813500  -0.273083   0.259167  -1.157354  ...       269.086     269.086     269.086  0.940184   1.217050  0.542815   0.702664      -1.046187       -1.171271      0
23      599 -0.078332  0.088286      0 -0.358417  0.072625 -0.877146   1.528271   9.225062   8.612792  ...       174.878     174.878     174.878  0.950327  12.712890  0.548672   7.339790      -1.162937       19.366125      0
24      624 -0.287427 -0.124020      0 -0.375625  0.003313 -0.881021   3.058979  13.556896  -2.243458  ...       141.342     141.342     141.342  0.957759  14.077638  0.552963   8.127728      -1.253333       14.372417      0
25      649 -0.133860 -0.209258      0 -0.217771 -0.061896 -0.906937   8.340938  13.584937  -3.330854  ...       209.939     209.939     209.939  0.934768  16.285465  0.539688   9.402417      -1.186604       18.595021      0
26      674  0.370613 -0.298640      0 -0.053958 -0.001812 -0.980750   3.474271   0.165229   0.699917  ...       210.670     210.670     210.670  0.982235   3.547921  0.567094   2.048393      -1.036521        4.339417      0
27      699  0.148621 -0.207852      0 -0.085833  0.108146 -0.964875   7.713375   3.545458  -3.878292  ...       258.659     258.659     258.659  0.974703   9.333144  0.562745   5.388493      -0.942562        7.380542      0
28      724  0.099657 -0.339254      0 -0.000479  0.009750 -0.933958  -3.399354  -1.110229 -13.351083  ...       270.244     270.244     270.244  0.934009  13.821709  0.539251   7.979967      -0.924687      -17.860667      0
29      749 -0.275138  0.177821      0 -0.080833  0.009521 -0.956729 -31.440542  -5.604646 -11.458333  ...       219.573     219.573     219.573  0.960185  33.929532  0.554363  19.589224      -1.028042      -48.503521      0
...     ...       ...       ...    ...       ...       ...       ...        ...        ...        ...  ...           ...         ...         ...       ...        ...       ...        ...            ...             ...    ...
5454  48074 -1.066465 -0.250198      0 -0.913688 -0.257562  0.381917  -0.622396  -0.815500   0.401437  ...         7.500       7.500       7.500  1.023242   1.101621  0.590769   0.636021      -0.789333       -1.036458      7
5455  48099 -1.095753 -0.233986      0 -0.910042 -0.251187  0.393542  -0.525854   0.496729   0.786396  ...         7.012       7.012       7.012  1.022813   1.068495  0.590521   0.616896      -0.767687        0.757271      7
5456  48124 -1.119910 -0.250395      0 -0.914313 -0.252167  0.384813  -0.133375   0.088917   0.252854  ...         6.159       6.159       6.159  1.023541   0.299383  0.590942   0.172849      -0.781667        0.208396      7
5457  48149 -1.069318 -0.250071      0 -0.898792 -0.259396  0.413167   0.689833  -1.834417  -0.261667  ...         8.171       8.171       8.171  1.022653   1.977226  0.590429   1.141552      -0.745021       -1.406250      7
5458  48174 -1.069752 -0.260435      0 -0.933250 -0.186875  0.340604   5.585687   9.424458   6.156000  ...        19.756      19.756      19.756  1.010885  12.566489  0.583635   7.255266      -0.779521       21.166146      7
5459  48199 -1.429273  0.053432      0 -0.999063 -0.012167  0.165938   5.190583  11.650146   8.664937  ...        24.939      24.939      24.939  1.012822  15.419118  0.584753   8.902232      -0.845292       25.505667      7
5460  48224 -1.415300  0.044516      0 -0.979021 -0.050896  0.205583  -4.419458  -6.955021  -6.079708  ...        23.475      23.475      23.475  1.001667  10.240448  0.578313   5.912326      -0.824333      -17.454188      7
5461  48249 -1.060437 -0.291727      0 -0.910167 -0.234562  0.381375  -4.582021  -9.766271  -7.028688  ...        19.451      19.451      19.451  1.014332  12.875458  0.585625   7.433649      -0.763354      -21.376979      7
5462  48274 -1.024455 -0.268518      0 -0.877292 -0.271917  0.451167   0.783896  -1.704771   0.877792  ...        17.621      17.621      17.621  1.023294   2.071534  0.590799   1.196001      -0.698042       -0.043083      7
5463  48299 -1.035420 -0.258163      0 -0.873688 -0.267042  0.462313  -0.027875   0.199375   0.423042  ...         8.171       8.171       8.171  1.023901   0.468499  0.591150   0.270488      -0.678417        0.594542      7
5464  48324 -1.029049 -0.268655      0 -0.879083 -0.267958  0.451625  -0.379792   0.539812   0.481500  ...         5.366       5.366       5.366  1.023989   0.816995  0.591201   0.471693      -0.695417        0.641521      7
5465  48349 -1.009006 -0.261509      0 -0.871771 -0.266542  0.465354   0.039333  -1.089958   0.458646  ...         5.853       5.853       5.853  1.023515   1.183179  0.590927   0.683109      -0.672958       -0.591979      7
5466  48374 -1.003390 -0.273162      0 -0.863417 -0.269000  0.480396  -0.109313  -0.222313   0.284583  ...         6.036       6.036       6.036  1.024026   0.377306  0.591222   0.217838      -0.652021       -0.047042      7
5467  48399 -1.007909 -0.267401      0 -0.871813 -0.270937  0.465188  -0.005104   1.189042   0.403958  ...         6.891       6.891       6.891  1.024629   1.255798  0.591570   0.725035      -0.677562        1.587896      7
5468  48424 -1.007634 -0.270142      0 -0.860708 -0.269625  0.484708   0.750792  -1.852083   0.147375  ...         6.891       6.891       6.891  1.023943   2.003901  0.591174   1.156953      -0.645625       -0.953917      7
5469  48449 -0.938304 -0.261053      0 -0.841667 -0.262625  0.516646   0.298562  -0.142167   0.240146  ...         9.878       9.878       9.878  1.021909   0.408682  0.589999   0.235953      -0.587646        0.396542      7
5470  48474 -1.076837 -0.271327      0 -0.894125 -0.274167  0.367021  -3.236792  11.736562   0.755896  ...        21.951      21.951      21.951  1.004655  12.198160  0.580038   7.042611      -0.801271        9.255667      7
5471  48499 -1.323072 -0.246296      0 -0.928208 -0.262542 -0.055062  -7.691875  25.766000   1.657875  ...        30.671      30.671      30.671  0.966194  26.940680  0.557832  15.554209      -1.245813       19.732000      7
5472  48524 -0.782937 -0.170726      0 -0.761729 -0.193458 -0.572667  -6.111583  26.330083   1.967812  ...        24.939      24.939      24.939  0.972422  27.101606  0.561428  15.647119      -1.527854       22.186312      7
5473  48549 -0.614800 -0.133733      0 -0.687938 -0.169479 -0.718896   1.262646  -5.533500  -0.081250  ...        27.744      27.744      27.744  1.009352   5.676310  0.582750   3.277219      -1.576313       -4.352104      7
5474  48574 -1.251782 -0.259116      0 -0.849063 -0.238187 -0.211167  10.304854 -38.348521  -2.343771  ...        23.231      23.231      23.231  0.906770  39.778038  0.523524  22.965861      -1.298417      -30.387438      7
5475  48599 -0.967152 -0.273931      0 -0.872563 -0.275896  0.380854   9.277125 -25.746896  -1.800021  ...        18.719      18.719      18.719  0.991228  27.426406  0.572286  15.834643      -0.767604      -18.269792      7
5476  48624 -0.931174 -0.277562      0 -0.827042 -0.275583  0.536521   1.783521  -2.585104  -0.208271  ...        15.183      15.183      15.183  1.023620   3.147553  0.590988   1.817240      -0.566104       -1.009854      7
5477  48649 -0.943571 -0.279720      0 -0.833167 -0.273812  0.529854  -1.257563  -0.106729   2.056583  ...        17.195      17.195      17.195  1.024639   2.412963  0.591576   1.393125      -0.577125        0.692292      7
5478  48674 -0.933760 -0.248164      0 -0.821396 -0.262521  0.552979  -0.989521  -2.319563   1.135625  ...        17.195      17.195      17.195  1.024399   2.765713  0.591437   1.596785      -0.530938       -2.173458      7
5479  48699 -0.903053 -0.261470      0 -0.812313 -0.266896  0.564333  -0.640208   0.296021  -0.102938  ...        10.792      10.792      10.792  1.024479   0.712805  0.591483   0.411538      -0.514875       -0.447125      7
5480  48724 -0.960065 -0.272204      0 -0.823104 -0.276437  0.543750  -0.605896   1.737792   0.283208  ...        10.487      10.487      10.487  1.024491   1.862052  0.591490   1.075056      -0.555792        1.415104      7
5481  48749 -0.956441 -0.279585      0 -0.824250 -0.282312  0.539188   0.224854  -1.074708   0.232500  ...         9.146       9.146       9.146  1.024603   1.122325  0.591555   0.647975      -0.567375       -0.617354      7
5482  48774 -0.901472 -0.268531      0 -0.818313 -0.278708  0.549604  -0.019104  -0.395083   0.497979  ...         8.171       8.171       8.171  1.024392   0.635955  0.591433   0.367169      -0.547417        0.083792      7
5483  48799 -0.944411 -0.274120      0 -0.825625 -0.284750  0.535604  -0.684771   1.789875   0.191750  ...         4.513       4.513       4.513  1.024505   1.925963  0.591498   1.111955      -0.574771        1.296854      7

[5367 rows x 56 columns]

``````
``````

In [177]:

ymount_td = combine_setState_createFeatures('your_mount_raw_data', 'your_mount')
ysc_td = combine_setState_createFeatures('your_side_control_raw_data', 'your_side_control')
ycg_td = combine_setState_createFeatures('your_closed_guard_raw_data', 'your_closed_guard')
ybc_td = combine_setState_createFeatures('your_back_control_raw_data', 'your_back_control')
#5 Opponent mount or opponent side control
omountsc_td = combine_setState_createFeatures('opponent_mount_and_opponent_side_control_raw_data', 'opponent_mount_or_sc')
#6 Opponent closed guard
ocg_td = combine_setState_createFeatures('opponent_closed_guard_raw_data', 'opponent_closed_guard')
#7 Opponent back control
obc_td = combine_setState_createFeatures('opponent_back_control_raw_data', 'opponent_back_control')
#8 "Non jiu-jitsu" motion
nonjj_td = combine_setState_createFeatures('non_jj_raw_data', 'non_jj')

``````
``````

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

``````
``````

In [166]:

test_data1 = prep_test('test1_ymount_ycg.csv')
test_data4 = prep_test('GL_TEST1_CS.csv')
test_data5 = prep_test('GL_TEST2_CS.csv')
test_data6 = prep_test('GL_TEST3_CS_very_still.csv')
test_data7 = prep_test('GL_TEST1_UrsWearing.csv')

test_data100 = prep_test('CS_OCG_STAND_OCG.csv')

``````
``````

A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

Removed 2 NaN rows
Removed 2 NaN rows
Removed 1 NaN rows
Removed 5 NaN rows
Removed 1 NaN rows
index   ACCEL_X   ACCEL_Y   ACCEL_Z    GYRO_X    GYRO_Y  GYRO_Z  rolling_median_x  rolling_median_y  rolling_median_z  ...    rolling_std_y  rolling_std_z  rolling_std_gx  rolling_std_gy  rolling_std_gz   acc_rss  gyro_rss  acc_rms  gyro_rms  state
0     14 -0.957107 -0.142429  0.343357  1.631143 -3.353714   2.633           -0.9605            -0.147             0.341  ...         0.023024       0.028165        3.959507        3.959507        3.959507  1.026759  4.565163   0.5928  2.635698      0

[1 rows x 36 columns]
ACCEL_X   ACCEL_Y   ACCEL_Z  GYRO_X    GYRO_Y    GYRO_Z  rolling_median_x  rolling_median_y  rolling_median_z  rolling_median_gx    ...     rolling_std_x  rolling_std_y  rolling_std_z  rolling_std_gx  rolling_std_gy  rolling_std_gz   acc_rss  gyro_rss   acc_rms  gyro_rms
14 -0.973643 -0.279821  0.168107    0.44  0.568429  0.723107           -0.9755           -0.2795             0.164              0.427    ...           0.00523       0.004372       0.012568         0.67042         0.67042         0.67042  1.026908  1.019605  0.592886  0.588669

[1 rows x 34 columns]

``````
``````

In [15]:

print training_data50.columns

``````
``````

Index([u'index', u'tiltx', u'tilty', u'ACCEL_X', u'ACCEL_Y', u'ACCEL_Z', u'GYRO_X', u'GYRO_Y', u'GYRO_Z', u'rolling_median_x', u'rolling_median_y', u'rolling_median_z', u'rolling_median_gx', u'rolling_median_gy', u'rolling_median_gz', u'rolling_max_x', u'rolling_max_y', u'rolling_max_z', u'rolling_max_gx', u'rolling_max_gy', u'rolling_max_gz', u'rolling_min_x', u'rolling_min_y', u'rolling_min_z', u'rolling_min_gx', u'rolling_min_gy', u'rolling_min_gz', u'rolling_sum_x', u'rolling_sum_y', u'rolling_sum_z', u'rolling_sum_gx', u'rolling_sum_gy', u'rolling_sum_gz', u'rolling_std_x', u'rolling_std_y', u'rolling_std_z', u'rolling_std_gx', u'rolling_std_gy', u'rolling_std_gz', u'avg_tiltx', u'avg_tilty', u'max_min_x', u'max_min_y', u'max_min_z', u'max_min_gx', u'max_min_gy', u'max_min_gz', u'acc_rss', u'gyro_rss', u'acc_rms', u'gyro_rms', u'acc_magnitude', u'gyro_magnitude', u'state'], dtype='object')

``````
``````

In [155]:

pre_smooth = trial(training_data, test_data1)

``````
``````

[7 7 7 7 0 5 5 5 5 0 0 5 5 5 5 5 0 0 5 5 5 0 5 7 7 5 5 5 5 7 7 5 5 0 1 1 6
3 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 2 2 2 2 2 2 4 2 2 2 2 2 2 4 4 4 4
4 4 4 4 4 4 4 4]
['OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'OTHER', 'OTHER', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'OTHER', 'OTHER', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'your_side_control', 'your_side_control', 'opponent_back_control', 'your_back_control', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc']
Opponent Mount or Opponent Side Control: 0.19512195122
Opponent Closed Guard: 0.231707317073
Opponent Back Control: 0.0121951219512
OTHER: 0.0975609756098

``````
``````

In [12]:

``````
``````

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-12-306fe3255187> in <module>()
----> 1 test_model_stand(training_data50) # newest with tilt

<ipython-input-8-1fd74f857e4f> in test_model_stand(df_train)
2     """check model accuracy"""
3
----> 4     y = df_train['stand'].values
5     X = df_train.drop(['stand', 'state', 'index'], axis=1)
6

1795             return self._getitem_multilevel(key)
1796         else:
-> 1797             return self._getitem_column(key)
1798
1799     def _getitem_column(self, key):

1802         # get column
1803         if self.columns.is_unique:
-> 1804             return self._get_item_cache(key)
1805
1806         # duplicate columns & possible reduce dimensionaility

1082         res = cache.get(item)
1083         if res is None:
-> 1084             values = self._data.get(item)
1085             res = self._box_item_values(item, values)
1086             cache[item] = res

2849
2850             if not isnull(item):
-> 2851                 loc = self.items.get_loc(item)
2852             else:
2853                 indexer = np.arange(len(self.items))[isnull(self.items)]

1570         """
1571         if method is None:
-> 1572             return self._engine.get_loc(_values_from_object(key))
1573
1574         indexer = self.get_indexer([key], method=method)

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3824)()

pandas/index.pyx in pandas.index.IndexEngine.get_loc (pandas/index.c:3704)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12280)()

pandas/hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:12231)()

KeyError: 'stand'

``````
``````

In [204]:

print '*** 30 rows ***'
test_model(training_data)

print '*** 10 rows ***'
test_model(training_data10)

print '*** 20 rows ***'
test_model(training_data20)

print '*** 26 rows ***'
test_model(training_data26)

print '*** 36 rows ***'
test_model(training_data36)

print '*** 40 rows ***'
test_model(training_data40)

print '*** 50 rows ***'
test_model(training_data50)

print '*** 60 rows ***'
test_model(training_data60)

print '*** 56 rows ***'
test_model(training_data56)

print '*** 64 rows ***'
test_model(training_data64)

print '*** 70 rows ***'
test_model(training_data70)

``````
``````

*** 30 rows ***
rf prediction: 0.810615199035
Random Forest Accuracy: 0.73 (+/- 0.15)
Feature ranking:
1. feature rolling_max_z (0.100646)
2. feature ACCEL_Z (0.087646)
3. feature rolling_median_z (0.077526)
4. feature rolling_min_z (0.067189)
5. feature ACCEL_X (0.064614)
6. feature rolling_max_x (0.060364)
7. feature rolling_median_x (0.055946)
8. feature rolling_min_x (0.051143)
9. feature acc_rms (0.040409)
11. feature rolling_std_x (0.033889)
12. feature ACCEL_Y (0.029493)
13. feature rolling_median_y (0.028272)
14. feature rolling_std_y (0.026964)
15. feature rolling_max_y (0.024981)
16. feature rolling_min_y (0.024974)
17. feature rolling_std_gz (0.020580)
18. feature rolling_std_gy (0.019857)
19. feature rolling_std_gx (0.019550)
20. feature rolling_std_z (0.018499)
21. feature rolling_min_gx (0.009949)
22. feature rolling_max_gx (0.009949)
23. feature rolling_max_gy (0.009839)
25. feature rolling_max_gz (0.009641)
26. feature gyro_rms (0.009547)
27. feature rolling_min_gy (0.009463)
28. feature rolling_min_gz (0.009357)
29. feature GYRO_Y (0.006623)
30. feature GYRO_X (0.005038)
31. feature rolling_median_gy (0.004582)
32. feature rolling_median_gx (0.004578)
33. feature rolling_median_gz (0.004571)
34. feature GYRO_Z (0.004507)
*** 10 rows ***
rf prediction: 0.800959232614
Random Forest Accuracy: 0.69 (+/- 0.16)
Feature ranking:
1. feature rolling_max_z (0.089464)
2. feature ACCEL_Z (0.082839)
3. feature rolling_median_z (0.079682)
4. feature ACCEL_X (0.068628)
5. feature rolling_min_z (0.067635)
6. feature rolling_median_x (0.061184)
7. feature rolling_max_x (0.059554)
8. feature rolling_min_x (0.054527)
9. feature rolling_std_x (0.045481)
10. feature rolling_std_y (0.033907)
11. feature rolling_std_z (0.031163)
12. feature ACCEL_Y (0.030227)
13. feature rolling_median_y (0.028829)
14. feature rolling_min_y (0.028702)
15. feature rolling_max_y (0.027918)
16. feature rolling_std_gy (0.021460)
17. feature rolling_std_gx (0.021381)
18. feature rolling_std_gz (0.019895)
19. feature gyro_rms (0.018262)
22. feature acc_rms (0.015937)
23. feature GYRO_Y (0.010971)
24. feature GYRO_Z (0.008386)
25. feature rolling_max_gx (0.006950)
26. feature rolling_max_gz (0.006948)
27. feature rolling_max_gy (0.006897)
28. feature rolling_min_gx (0.006375)
29. feature rolling_min_gz (0.006246)
30. feature rolling_min_gy (0.006194)
31. feature GYRO_X (0.005422)
32. feature rolling_median_gy (0.005065)
33. feature rolling_median_gx (0.004993)
34. feature rolling_median_gz (0.004900)
*** 20 rows ***
rf prediction: 0.802884615385
Random Forest Accuracy: 0.71 (+/- 0.15)
Feature ranking:
1. feature rolling_max_z (0.098546)
2. feature ACCEL_Z (0.089308)
3. feature rolling_median_z (0.079295)
4. feature rolling_min_z (0.068500)
5. feature ACCEL_X (0.065135)
6. feature rolling_max_x (0.059484)
7. feature rolling_median_x (0.057757)
8. feature rolling_min_x (0.051230)
9. feature rolling_std_x (0.040991)
10. feature rolling_std_y (0.032525)
11. feature ACCEL_Y (0.030590)
12. feature rolling_median_y (0.028329)
13. feature acc_rms (0.027649)
15. feature rolling_min_y (0.027086)
16. feature rolling_max_y (0.026972)
17. feature rolling_std_z (0.025566)
18. feature rolling_std_gz (0.020795)
19. feature rolling_std_gx (0.019664)
20. feature rolling_std_gy (0.019358)
22. feature gyro_rms (0.012821)
23. feature GYRO_Y (0.008507)
24. feature rolling_max_gz (0.008104)
25. feature rolling_max_gx (0.007858)
26. feature rolling_max_gy (0.007840)
27. feature rolling_min_gz (0.006576)
28. feature rolling_min_gx (0.006537)
29. feature rolling_min_gy (0.006279)
30. feature GYRO_Z (0.006209)
31. feature GYRO_X (0.005277)
32. feature rolling_median_gy (0.005029)
33. feature rolling_median_gz (0.005016)
34. feature rolling_median_gx (0.004934)
*** 26 rows ***
rf prediction: 0.791231732777
Random Forest Accuracy: 0.72 (+/- 0.15)
Feature ranking:
1. feature rolling_max_z (0.098693)
2. feature ACCEL_Z (0.087887)
3. feature rolling_median_z (0.077902)
4. feature rolling_min_z (0.068842)
5. feature ACCEL_X (0.063630)
6. feature rolling_max_x (0.060951)
7. feature rolling_median_x (0.056119)
8. feature rolling_min_x (0.051121)
9. feature rolling_std_x (0.039040)
11. feature acc_rms (0.034864)
12. feature rolling_std_y (0.031352)
13. feature ACCEL_Y (0.030592)
14. feature rolling_median_y (0.027059)
15. feature rolling_max_y (0.026216)
16. feature rolling_min_y (0.026133)
17. feature rolling_std_gx (0.020209)
18. feature rolling_std_gz (0.019481)
19. feature rolling_std_gy (0.019188)
20. feature rolling_std_z (0.019025)
21. feature gyro_rms (0.010415)
23. feature rolling_max_gx (0.009905)
24. feature rolling_max_gy (0.009724)
25. feature rolling_max_gz (0.009401)
26. feature rolling_min_gx (0.008009)
27. feature rolling_min_gz (0.007938)
28. feature rolling_min_gy (0.007885)
29. feature GYRO_Y (0.007418)
30. feature GYRO_X (0.005203)
31. feature GYRO_Z (0.005201)
32. feature rolling_median_gy (0.004660)
33. feature rolling_median_gx (0.004659)
34. feature rolling_median_gz (0.004631)
*** 36 rows ***
rf prediction: 0.811594202899
Random Forest Accuracy: 0.74 (+/- 0.14)
Feature ranking:
1. feature rolling_max_z (0.102962)
2. feature ACCEL_Z (0.087586)
3. feature rolling_median_z (0.079168)
4. feature rolling_min_z (0.066650)
5. feature ACCEL_X (0.064979)
6. feature rolling_max_x (0.062869)
7. feature rolling_median_x (0.055817)
8. feature rolling_min_x (0.048738)
9. feature acc_rms (0.040374)
11. feature rolling_std_x (0.033492)
12. feature ACCEL_Y (0.029549)
13. feature rolling_median_y (0.028143)
14. feature rolling_std_y (0.027482)
15. feature rolling_min_y (0.025169)
16. feature rolling_max_y (0.024220)
17. feature rolling_std_gy (0.019574)
18. feature rolling_std_gz (0.019328)
19. feature rolling_std_gx (0.018373)
20. feature rolling_std_z (0.017382)
21. feature rolling_max_gx (0.011005)
22. feature rolling_min_gy (0.010928)
23. feature rolling_min_gx (0.010718)
24. feature rolling_max_gz (0.010375)
25. feature rolling_min_gz (0.010355)
26. feature rolling_max_gy (0.009867)
28. feature gyro_rms (0.008670)
29. feature GYRO_Y (0.005876)
30. feature GYRO_X (0.004807)
31. feature rolling_median_gz (0.004268)
32. feature rolling_median_gy (0.004259)
33. feature rolling_median_gx (0.004199)
34. feature GYRO_Z (0.004136)
*** 40 rows ***
rf prediction: 0.808064516129
Random Forest Accuracy: 0.75 (+/- 0.15)
Feature ranking:
1. feature rolling_max_z (0.101670)
2. feature ACCEL_Z (0.087326)
3. feature rolling_median_z (0.078171)
4. feature rolling_min_z (0.069127)
5. feature ACCEL_X (0.063274)
6. feature rolling_max_x (0.060005)
7. feature rolling_median_x (0.054192)
8. feature rolling_min_x (0.048366)
10. feature acc_rms (0.042417)
11. feature rolling_std_x (0.031020)
12. feature ACCEL_Y (0.029493)
13. feature rolling_median_y (0.028651)
14. feature rolling_std_y (0.028226)
15. feature rolling_max_y (0.024177)
16. feature rolling_min_y (0.024117)
17. feature rolling_std_gz (0.020776)
18. feature rolling_std_gy (0.020449)
19. feature rolling_std_gx (0.019703)
20. feature rolling_std_z (0.015980)
21. feature rolling_max_gy (0.012712)
22. feature rolling_max_gz (0.012569)
23. feature rolling_max_gx (0.012219)
24. feature rolling_min_gx (0.009780)
25. feature rolling_min_gz (0.009582)
26. feature rolling_min_gy (0.009407)
27. feature gyro_rms (0.008137)
29. feature GYRO_Y (0.006029)
30. feature GYRO_X (0.005056)
31. feature GYRO_Z (0.004179)
32. feature rolling_median_gy (0.004155)
33. feature rolling_median_gx (0.004112)
34. feature rolling_median_gz (0.004107)
*** 50 rows ***
rf prediction: 0.826262626263
Random Forest Accuracy: 0.75 (+/- 0.13)
Feature ranking:
1. feature rolling_max_z (0.103500)
2. feature ACCEL_Z (0.090047)
3. feature rolling_median_z (0.079806)
4. feature ACCEL_X (0.066519)
5. feature rolling_min_z (0.066011)
6. feature rolling_max_x (0.060945)
7. feature rolling_median_x (0.052998)
8. feature rolling_min_x (0.050469)
10. feature acc_rms (0.043259)
11. feature rolling_std_x (0.030966)
12. feature rolling_median_y (0.027746)
13. feature ACCEL_Y (0.027664)
14. feature rolling_max_y (0.024803)
15. feature rolling_std_y (0.024480)
16. feature rolling_min_y (0.022475)
17. feature rolling_std_gz (0.020139)
18. feature rolling_std_gy (0.019673)
19. feature rolling_std_gx (0.019011)
20. feature rolling_std_z (0.015849)
21. feature rolling_max_gx (0.012532)
22. feature rolling_max_gy (0.012443)
23. feature rolling_max_gz (0.011480)
24. feature rolling_min_gx (0.011030)
25. feature rolling_min_gy (0.010424)
26. feature rolling_min_gz (0.010036)
27. feature gyro_rms (0.008350)
29. feature GYRO_Y (0.004905)
30. feature GYRO_X (0.004762)
31. feature GYRO_Z (0.003943)
32. feature rolling_median_gx (0.003897)
33. feature rolling_median_gy (0.003855)
34. feature rolling_median_gz (0.003811)
*** 60 rows ***
rf prediction: 0.83698296837
Random Forest Accuracy: 0.76 (+/- 0.13)
Feature ranking:
1. feature rolling_max_z (0.104726)
2. feature ACCEL_Z (0.087126)
3. feature rolling_median_z (0.079576)
4. feature rolling_min_z (0.069418)
5. feature ACCEL_X (0.064232)
6. feature rolling_max_x (0.059813)
7. feature rolling_median_x (0.052843)
8. feature rolling_min_x (0.048988)
10. feature acc_rms (0.046530)
11. feature ACCEL_Y (0.026955)
12. feature rolling_median_y (0.026871)
13. feature rolling_std_x (0.026059)
14. feature rolling_max_y (0.023962)
15. feature rolling_std_y (0.022477)
16. feature rolling_min_y (0.022035)
17. feature rolling_std_gy (0.021415)
18. feature rolling_std_gz (0.021040)
19. feature rolling_std_gx (0.019715)
20. feature rolling_std_z (0.016232)
21. feature rolling_max_gz (0.013889)
22. feature rolling_max_gy (0.013139)
23. feature rolling_max_gx (0.013088)
24. feature rolling_min_gz (0.011377)
25. feature rolling_min_gy (0.011207)
26. feature rolling_min_gx (0.011182)
28. feature gyro_rms (0.007672)
29. feature GYRO_Y (0.005013)
30. feature GYRO_X (0.004235)
31. feature GYRO_Z (0.003438)
32. feature rolling_median_gy (0.003426)
33. feature rolling_median_gz (0.003422)
34. feature rolling_median_gx (0.003384)
*** 56 rows ***
rf prediction: 0.816326530612
Random Forest Accuracy: 0.75 (+/- 0.13)
Feature ranking:
1. feature rolling_max_z (0.101267)
2. feature ACCEL_Z (0.092868)
3. feature rolling_median_z (0.077312)
4. feature rolling_min_z (0.070636)
5. feature ACCEL_X (0.066966)
6. feature rolling_max_x (0.061292)
7. feature rolling_median_x (0.053524)
8. feature rolling_min_x (0.047428)
10. feature acc_rms (0.043411)
11. feature ACCEL_Y (0.028172)
12. feature rolling_median_y (0.027698)
13. feature rolling_std_x (0.027267)
14. feature rolling_max_y (0.024520)
15. feature rolling_std_y (0.023518)
16. feature rolling_min_y (0.022885)
17. feature rolling_std_gy (0.021073)
18. feature rolling_std_gx (0.020541)
19. feature rolling_std_gz (0.020238)
20. feature rolling_std_z (0.016658)
21. feature rolling_min_gz (0.012928)
22. feature rolling_min_gy (0.012392)
23. feature rolling_min_gx (0.012094)
24. feature rolling_max_gy (0.011247)
25. feature rolling_max_gx (0.010766)
26. feature rolling_max_gz (0.010575)
28. feature gyro_rms (0.007602)
29. feature GYRO_X (0.005083)
30. feature GYRO_Y (0.004223)
31. feature rolling_median_gz (0.003678)
32. feature rolling_median_gx (0.003671)
33. feature rolling_median_gy (0.003540)
34. feature GYRO_Z (0.003394)
*** 64 rows ***
rf prediction: 0.828571428571
Random Forest Accuracy: 0.76 (+/- 0.13)
Feature ranking:
1. feature rolling_max_z (0.104010)
2. feature ACCEL_Z (0.091539)
3. feature rolling_median_z (0.081851)
4. feature rolling_min_z (0.068738)
5. feature ACCEL_X (0.065797)
6. feature rolling_max_x (0.062562)
7. feature rolling_median_x (0.052209)
8. feature acc_rms (0.045524)
10. feature rolling_min_x (0.044215)
11. feature rolling_std_x (0.027046)
12. feature ACCEL_Y (0.026750)
13. feature rolling_median_y (0.026077)
14. feature rolling_max_y (0.025093)
15. feature rolling_std_y (0.023375)
16. feature rolling_min_y (0.022309)
17. feature rolling_std_gz (0.021353)
18. feature rolling_std_gy (0.020880)
19. feature rolling_std_gx (0.020552)
20. feature rolling_std_z (0.015625)
21. feature rolling_max_gx (0.012570)
22. feature rolling_max_gz (0.012336)
23. feature rolling_min_gx (0.011721)
24. feature rolling_min_gz (0.011674)
25. feature rolling_min_gy (0.011612)
26. feature rolling_max_gy (0.011607)
28. feature gyro_rms (0.008283)
29. feature GYRO_X (0.004370)
30. feature GYRO_Y (0.004322)
31. feature GYRO_Z (0.003344)
32. feature rolling_median_gz (0.003166)
33. feature rolling_median_gy (0.003121)
34. feature rolling_median_gx (0.003117)
*** 70 rows ***
rf prediction: 0.832386363636
Random Forest Accuracy: 0.76 (+/- 0.12)
Feature ranking:
1. feature rolling_max_z (0.098302)
2. feature ACCEL_Z (0.091922)
3. feature rolling_median_z (0.081628)
4. feature ACCEL_X (0.068240)
5. feature rolling_min_z (0.068104)
6. feature rolling_max_x (0.061481)
7. feature rolling_median_x (0.054829)
8. feature rolling_min_x (0.046176)
9. feature acc_rms (0.045980)
11. feature rolling_max_y (0.025905)
12. feature ACCEL_Y (0.025563)
13. feature rolling_median_y (0.025487)
14. feature rolling_std_x (0.023889)
15. feature rolling_min_y (0.022483)
16. feature rolling_std_gz (0.022034)
17. feature rolling_std_y (0.021972)
18. feature rolling_std_gy (0.021518)
19. feature rolling_std_gx (0.021482)
20. feature rolling_std_z (0.014809)
21. feature rolling_max_gy (0.012597)
22. feature rolling_max_gx (0.012545)
23. feature rolling_min_gz (0.012171)
24. feature rolling_min_gx (0.011985)
25. feature rolling_max_gz (0.011959)
26. feature rolling_min_gy (0.011658)
27. feature gyro_rms (0.008552)
29. feature GYRO_X (0.004983)
30. feature GYRO_Y (0.004046)
31. feature rolling_median_gx (0.003526)
32. feature rolling_median_gz (0.003412)
33. feature rolling_median_gy (0.003408)
34. feature GYRO_Z (0.003170)

``````
``````

In [205]:

print training_data70

``````
``````

index   ACCEL_X   ACCEL_Y   ACCEL_Z    GYRO_X     GYRO_Y    GYRO_Z  rolling_median_x  rolling_median_y  rolling_median_z  ...    rolling_std_y  rolling_std_z  rolling_std_gx  rolling_std_gy  rolling_std_gz   acc_rss   gyro_rss   acc_rms   gyro_rms  state
0        34 -0.960191 -0.159721  0.313868 -2.416632   0.650971 -1.270588           -0.9620           -0.1450            0.3145  ...         0.060615       0.053717       13.342895       13.342895       13.342895  1.022737   2.806825  0.590477   1.620521      0
1        69 -0.982074 -0.210000  0.102059 -5.165956   7.133265  0.401721           -0.9820           -0.1740            0.0570  ...         0.082127       0.179222       19.247336       19.247336       19.247336  1.009448   8.816572  0.582805   5.090250      0
2       104 -1.008515 -0.184912 -0.044074 -0.022397   4.052206  4.580324           -1.0115           -0.1565           -0.0300  ...         0.067879       0.045111       14.121794       14.121794       14.121794  1.026273   6.115573  0.592519   3.530828      0
3       139 -1.015176 -0.152309 -0.007074  0.073500  -0.937971  1.153162           -1.0155           -0.1560           -0.0050  ...         0.016627       0.021366        3.891751        3.891751        3.891751  1.026563   1.488279  0.592686   0.859258      0
4       174 -1.011485 -0.152265  0.062794  1.745809  -3.368044  1.022324           -1.0140           -0.1590            0.0395  ...         0.040219       0.067630        4.679675        4.679675        4.679675  1.024807   3.928959  0.591673   2.268385      0
5       209 -1.008868 -0.145838  0.109500  0.451868   0.016147  0.243015           -1.0130           -0.1580            0.1110  ...         0.047927       0.058597        8.933034        8.933034        8.933034  1.025218   0.513324  0.591910   0.296368      0
6       244 -1.011338 -0.152309  0.091368 -1.400676   2.006838 -0.829485           -1.0120           -0.1590            0.0800  ...         0.030541       0.052417        7.232336        7.232336        7.232336  1.026816   2.584055  0.592832   1.491905      0
7       279 -1.010926 -0.164500  0.074382 -0.184691   0.365882  0.278015           -1.0120           -0.1670            0.0735  ...         0.012990       0.036140        1.852105        1.852105        1.852105  1.026920   0.495250  0.592893   0.285933      0
8       314 -1.011838 -0.168368  0.053426 -0.256456   0.772103  0.329206           -1.0120           -0.1680            0.0520  ...         0.002608       0.014139        0.777181        0.777181        0.777181  1.027141   0.877661  0.593020   0.506718      0
9       349 -1.012721 -0.165750  0.033750 -0.224221   0.868897  0.590176           -1.0130           -0.1660            0.0305  ...         0.004068       0.017010        0.821403        0.821403        0.821403  1.026750   1.074042  0.592794   0.620098      0
10      384 -1.011838 -0.164868  0.014794 -0.578426   2.065044 -0.335309           -1.0130           -0.1620            0.0225  ...         0.023757       0.045000        2.839034        2.839034        2.839034  1.025289   2.170580  0.591951   1.253185      0
11      419 -1.011500 -0.147779 -0.033544  0.646485   1.083176  3.358985           -1.0125           -0.1485           -0.0605  ...         0.071261       0.063230        4.704303        4.704303        4.704303  1.022788   3.588035  0.590507   2.071553      0
12      454 -1.011941 -0.089809  0.028176 -0.576574  -3.996603  2.208574           -1.0120           -0.0835           -0.0070  ...         0.084651       0.106815        6.474305        6.474305        6.474305  1.016309   4.602507  0.586766   2.657259      0
13      489 -1.011441 -0.107426  0.042426 -2.882882   0.286103 -0.550529           -1.0105           -0.1155            0.0360  ...         0.074967       0.097266        7.838282        7.838282        7.838282  1.018015   2.948889  0.587751   1.702542      0
14      524 -1.017882 -0.071221 -0.037059  4.268221   4.273735  4.516706           -1.0170           -0.0545           -0.0300  ...         0.088326       0.052603        9.280327        9.280327        9.280327  1.021044   7.542092  0.589500   4.354429      0
15      559 -1.016574  0.012897 -0.098676  2.374382   1.219529  0.309353           -1.0155            0.0150           -0.1290  ...         0.051083       0.087720       10.148440       10.148440       10.148440  1.021433   2.687125  0.589725   1.551413      0
16      594 -1.010603 -0.072603 -0.107662 -5.147059  -0.659926 -4.313162           -1.0085           -0.0920           -0.1290  ...         0.099709       0.072855        7.069291        7.069291        7.069291  1.018911   6.747672  0.588269   3.895770      0
17      629 -0.999397 -0.160015 -0.109471 -8.072985   2.983353 -3.078353           -1.0030           -0.1650           -0.1005  ...         0.032583       0.091667        8.689036        8.689036        8.689036  1.018029   9.140555  0.587759   5.277302      0
18      664 -0.920809 -0.131824 -0.349971 -7.134176   8.427176 -0.347912           -0.9070           -0.1330           -0.4150  ...         0.042299       0.202276       10.524675       10.524675       10.524675  0.993854  11.046937  0.573802   6.377952      0
19      699 -0.713912 -0.097074 -0.684265 -2.074971  12.645279  0.673441           -0.6645           -0.1045           -0.7415  ...         0.033578       0.156202        8.789682        8.789682        8.789682  0.993635  12.832074  0.573676   7.408601      0
20      734 -0.579926 -0.096221 -0.821941 -0.924529   6.047382 -0.160441           -0.5810           -0.1020           -0.8180  ...         0.026412       0.033755        5.787349        5.787349        5.787349  1.010525   6.119749  0.583427   3.533239      0
21      769 -0.636809 -0.116485 -0.761412  2.617456  -7.888235  1.277838           -0.5835           -0.1060           -0.8135  ...         0.024467       0.122810        6.945023        6.945023        6.945023  0.999421   8.408817  0.577016   4.854833      0
22      804 -0.729544 -0.072441 -0.672221  7.840779  -1.607706  6.570132           -0.6995           -0.1020           -0.7290  ...         0.072220       0.161806       12.580696       12.580696       12.580696  0.994667  10.355152  0.574271   5.978550      0
23      839 -0.711882 -0.032353 -0.708750  4.310485   7.503618  4.496941           -0.6835           -0.0435           -0.7455  ...         0.040979       0.115254       11.276064       11.276064       11.276064  1.005062   9.752284  0.580273   5.630484      0
24      874 -0.676618 -0.045647 -0.751529 -0.248412   0.116603 -0.092353           -0.6760           -0.0430           -0.7500  ...         0.009864       0.011421        2.047521        2.047521        2.047521  1.012270   0.289541  0.584435   0.167166      0
25      909 -0.681618 -0.039544 -0.748118 -0.263662  -0.222294  0.450265           -0.6810           -0.0400           -0.7490  ...         0.004202       0.009510        0.951785        0.951785        0.951785  1.012841   0.567160  0.584764   0.327450      0
26      944 -0.687015 -0.040176 -0.743912  0.004515   0.038603  0.402779           -0.6870           -0.0400           -0.7440  ...         0.003464       0.008329        0.562890        0.562890        0.562890  1.013414   0.404650  0.585095   0.233625      0
27      979 -0.694221 -0.042412 -0.736853  0.037706   0.083412  0.430559           -0.6915           -0.0420           -0.7385  ...         0.006184       0.012033        0.839285        0.839285        0.839285  1.013259   0.440182  0.585005   0.254139      0
28     1014 -0.700221 -0.039544 -0.732059 -0.121912  -0.292279  0.724588           -0.7000           -0.0420           -0.7305  ...         0.009429       0.011096        1.117233        1.117233        1.117233  1.013796   0.790770  0.585316   0.456552      0
29     1049 -0.700382 -0.030132 -0.732676 -0.216088  -0.147941  0.741588           -0.6990           -0.0290           -0.7335  ...         0.008065       0.011268        0.990342        0.990342        0.990342  1.014031   0.786469  0.585451   0.454068      0
...     ...       ...       ...       ...       ...        ...       ...               ...               ...               ...  ...              ...            ...             ...             ...             ...       ...        ...       ...        ...    ...
3567  45009 -0.908868 -0.246926  0.398088 -0.069015  -0.045721  0.628588           -0.9080           -0.2460            0.4025  ...         0.009679       0.023650        3.521541        3.521541        3.521541  1.022491   0.634016  0.590335   0.366049      7
3568  45044 -0.916382 -0.246235  0.385294  0.230471   0.199882  0.170353           -0.9145           -0.2470            0.3905  ...         0.007906       0.024327        2.579916        2.579916        2.579916  1.024129   0.349413  0.591281   0.201734      7
3569  45079 -0.908912 -0.251044  0.395441 -0.137191  -0.569456  0.155985           -0.9080           -0.2505            0.3970  ...         0.010039       0.029884        2.378016        2.378016        2.378016  1.022505   0.606162  0.590344   0.349968      7
3570  45114 -0.909000 -0.253221  0.392221 -0.214294   0.190956  0.521853           -0.9090           -0.2545            0.3845  ...         0.011210       0.038614        2.442711        2.442711        2.442711  1.021880   0.595581  0.589983   0.343859      7
3571  45149 -0.933515 -0.236897  0.337412  1.276059   2.258750  2.443544           -0.9380           -0.2520            0.3425  ...         0.043550       0.067794        4.288816        4.288816        4.288816  1.020498   3.563872  0.589185   2.057603      7
3572  45184 -0.955132 -0.214485  0.294706  0.518382   1.687544  0.672603           -0.9635           -0.2205            0.2875  ...         0.044837       0.059862        4.780064        4.780064        4.780064  1.022318   1.889159  0.590235   1.090706      7
3573  45219 -0.936412 -0.233235  0.330647 -0.966559  -2.194221 -1.390809           -0.9350           -0.2390            0.3345  ...         0.027509       0.082367        4.624870        4.624870        4.624870  1.020095   2.771857  0.588952   1.600332      7
3574  45254 -0.921426 -0.250147  0.364897  0.334471  -1.350397  0.329926           -0.9240           -0.2500            0.3675  ...         0.013179       0.062236        3.003730        3.003730        3.003730  1.022130   1.429788  0.590127   0.825489      7
3575  45289 -0.917485 -0.254162  0.374926 -0.122853  -0.781015  0.274397           -0.9145           -0.2565            0.3840  ...         0.012710       0.037316        1.960822        1.960822        1.960822  1.023204   0.836881  0.590747   0.483174      7
3576  45324 -0.912029 -0.253162  0.387485 -0.470721  -0.757691  0.666294           -0.9125           -0.2520            0.3905  ...         0.010858       0.031410        1.527479        1.527479        1.527479  1.022758   1.113383  0.590489   0.642812      7
3577  45359 -0.909515 -0.253279  0.395191  0.227794  -0.165015  0.139912           -0.9060           -0.2540            0.3975  ...         0.009893       0.032225        1.899527        1.899527        1.899527  1.023496   0.314158  0.590916   0.181379      7
3578  45394 -0.922912 -0.211147  0.363118  4.115897   5.390897  4.141868           -0.9050           -0.2545            0.4035  ...         0.088539       0.095725        5.061890        5.061890        5.061890  1.014004   7.947166  0.585435   4.588298      7
3579  45429 -0.979691 -0.052338  0.214426  3.151044   6.761088  4.598324           -1.0050            0.0045            0.1650  ...         0.134073       0.131022        6.735429        6.735429        6.735429  1.004247   8.762761  0.579802   5.059182      7
3580  45464 -0.952985 -0.113735  0.266397 -3.565235  -5.713779 -3.755324           -0.9580           -0.1355            0.3045  ...         0.168256       0.150168        6.321781        6.321781        6.321781  0.996034   7.711072  0.575061   4.451990      7
3581  45499 -0.883147 -0.272191  0.437426 -1.969985  -5.114809 -2.679324           -0.8820           -0.2730            0.4455  ...         0.018871       0.034385        5.912879        5.912879        5.912879  1.022438   6.100892  0.590305   3.522352      7
3582  45534 -0.875735 -0.268132  0.458250  0.182074   0.254618  0.533574           -0.8760           -0.2695            0.4575  ...         0.007356       0.014908        1.801497        1.801497        1.801497  1.024110   0.618613  0.591270   0.357156      7
3583  45569 -0.873176 -0.267676  0.462309 -0.210750  -0.422382  0.382941           -0.8740           -0.2685            0.4585  ...         0.007376       0.022201        1.267338        1.267338        1.267338  1.023629   0.607837  0.590993   0.350935      7
3584  45604 -0.866676 -0.268500  0.474765 -0.132750  -0.273500  0.303103           -0.8675           -0.2685            0.4760  ...         0.007762       0.018668        1.435619        1.435619        1.435619  1.024022   0.429297  0.591220   0.247855      7
3585  45639 -0.864632 -0.270412  0.477824  0.351500  -0.644676  0.260074           -0.8645           -0.2700            0.4735  ...         0.006129       0.028255        1.728554        1.728554        1.728554  1.024220   0.778973  0.591334   0.449740      7
3586  45674 -0.854985 -0.266221  0.492809 -0.018809   0.911147  0.425059           -0.8460           -0.2670            0.5135  ...         0.012119       0.045633        2.575016        2.575016        2.575016  1.022122   1.005593  0.590123   0.580580      7
3587  45709 -0.902324 -0.269382  0.273897 -4.115853  13.870176  1.104779           -0.9145           -0.2670            0.3310  ...         0.021051       0.267737        6.821711        6.821711        6.821711  0.980701  14.510085  0.566208   8.377402      7
3588  45744 -0.823985 -0.223485 -0.339059 -6.559397  25.122853  1.744147           -0.8900           -0.2335           -0.3085  ...         0.058541       0.413011        6.632969        6.632969        6.632969  0.918618  26.023556  0.530364  15.024707      7
3589  45779 -0.746353 -0.188603 -0.609074  1.362059  -4.481662 -0.111132           -0.7195           -0.1735           -0.7280  ...         0.047961       0.260130        9.899432        9.899432        9.899432  0.981623   4.685387  0.566740   2.705109      7
3590  45814 -0.837574 -0.244103 -0.008779  8.680897 -30.697574 -1.739574           -0.8430           -0.2625            0.1395  ...         0.051541       0.517858        6.324316        6.324316        6.324316  0.872464  31.948789  0.503717  18.445642      7
3591  45849 -0.842882 -0.279441  0.493162  3.402941 -10.573897 -0.307559           -0.8325           -0.2785            0.5250  ...         0.017432       0.093803        5.479804        5.479804        5.479804  1.015749  11.112241  0.586443   6.415655      7
3592  45884 -0.829294 -0.271824  0.536897 -1.074191  -0.484221  0.990853           -0.8300           -0.2700            0.5360  ...         0.019754       0.017001        4.122904        4.122904        4.122904  1.024634   1.539528  0.591573   0.888847      7
3593  45919 -0.818500 -0.263147  0.556926 -0.335309  -0.366706  0.676956           -0.8180           -0.2650            0.5565  ...         0.010474       0.019476        3.398990        3.398990        3.398990  1.024381   0.839747  0.591426   0.484828      7
3594  45954 -0.821103 -0.275500  0.546574 -0.475191   1.362985  0.258191           -0.8235           -0.2780            0.5395  ...         0.010305       0.022552        2.606160        2.606160        2.606160  1.024135   1.466355  0.591285   0.846601      7
3595  45989 -0.821912 -0.280426  0.543441 -0.143485  -0.254662  0.457324           -0.8205           -0.2800            0.5460  ...         0.005109       0.016843        2.193928        2.193928        2.193928  1.024454   0.542757  0.591469   0.313361      7
3596  46024 -0.822529 -0.284044  0.540382 -0.245794   0.336279  0.246559           -0.8235           -0.2805            0.5430  ...         0.008512       0.019925        1.436643        1.436643        1.436643  1.024328   0.484035  0.591396   0.279458      7

[3511 rows x 36 columns]

``````
``````

In [180]:

pre_smooth2 = trial(training_data, test_data4)

``````
``````

[7 7 7 3 3 3 6 3 3 3 3 3 3 3 3 3 3 7 5 0 0 0 0 0 0 0 0 0 0 0 0 5 0 0 1 1 1
0 1 1 1 6 6 1 1 1 1 1 1 1 1 1 0 0 7 4 4 4 4 4 4 4 4 2 4 2 2 2 4 4 4 4 2 2
3 2 2 2 2 2 2 2 4 4 2 7 0 5 0 0 0 5 5 5 5 5 5 5 1 5 0 5 5 0 0 5 5 7 2 4 4
4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 2 6 6 6 6 4 4 6 6 6 6 6 6 6
6 6 6 6 6 6 6 6 6 6 1 0 0 7]
['OTHER', 'OTHER', 'OTHER', 'your_back_control', 'your_back_control', 'your_back_control', 'opponent_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'OTHER', 'opponent_closed_guard', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'opponent_closed_guard', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'your_mount', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_back_control', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'OTHER', 'your_mount', 'opponent_closed_guard', 'your_mount', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_side_control', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'OTHER', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'your_mount', 'your_mount', 'OTHER']
Opponent Mount or Opponent Side Control: 0.259259259259
Opponent Closed Guard: 0.0925925925926
Opponent Back Control: 0.148148148148
OTHER: 0.0493827160494

``````
``````

In [9]:

#pre_smooth3 = trial(training_data, test_data5)

``````
``````

[7 7 6 1 6 6 1 6 6 6 3 3 3 1 0 5 0 0 0 0 1 1 1 1 1 1 1 0 4 2 2 2 2 2 2 2 2
2 2 2 7 0 5 5 0 5 5 5 5 5 5 5 5 7 4 4 4 4 2 4 2 4 4 4 2 4 4 4 4 2 4 3 6 2
4 6 3 4 6 6 6 6 6 6 6 6 6 7 5 7]
['OTHER', 'OTHER', 'opponent_back_control', 'your_side_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_side_control', 'your_mount', 'opponent_closed_guard', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'your_back_control', 'opponent_back_control', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_back_control', 'your_back_control', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'OTHER', 'opponent_closed_guard', 'OTHER']
Opponent Mount or Opponent Side Control: 0.177777777778
Opponent Closed Guard: 0.133333333333
Opponent Back Control: 0.188888888889
OTHER: 0.0666666666667

``````
``````

In [10]:

#pre_smooth4 = trial(training_data, test_data6)

``````
``````

[7 7 7 6 3 3 6 6 6 3 3 3 5 0 0 5 5 5 5 5 5 7 7 7 7 5 7 5 0 1 1 1 1 1 1 1 1
1 1 1 5 7 2 2 2 2 7 7 7 2 2 2 7 7 7 2 2 7 7 7 7 2 2 2 5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 0 5 7 4 4 4 4 4 7 7 7 7 7 7 7 7 7 2 4 4 4 4 4 4 4 4 7 4 7 7 7 7
4 4 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 0 7 5]
['OTHER', 'OTHER', 'OTHER', 'opponent_back_control', 'your_back_control', 'your_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'opponent_closed_guard', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'opponent_closed_guard', 'OTHER', 'opponent_closed_guard', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'opponent_closed_guard', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'OTHER', 'opponent_mount_or_sc', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_mount', 'your_mount', 'OTHER', 'opponent_closed_guard']
Opponent Mount or Opponent Side Control: 0.118518518519
Opponent Closed Guard: 0.2
Opponent Back Control: 0.155555555556
OTHER: 0.259259259259

``````
``````

In [11]:

#pre_smooth5 = trial(training_data, test_data7)

``````
``````

[7 7 2 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 4 4 4 4 4 4 2 2 4 4
1 0 0 0 3 0 0 0 5 0 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 6 3 6 6 6 6 6 6 6 6 6
1 1 5 7]
['OTHER', 'OTHER', 'your_closed_guard', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_side_control', 'your_mount', 'your_mount', 'your_mount', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'opponent_closed_guard', 'your_mount', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'your_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'your_side_control', 'opponent_closed_guard', 'OTHER']
Opponent Mount or Opponent Side Control: 0.294871794872
Opponent Closed Guard: 0.025641025641
Opponent Back Control: 0.128205128205
OTHER: 0.0384615384615

``````
``````

In [110]:

print pre_smooth
pre_smooth_words = convert_to_words(pre_smooth)
pre_smooth_words2 = convert_to_words(pre_smooth2)
#pre_smooth_words3 = convert_to_words(pre_smooth3)
#pre_smooth_words4 = convert_to_words(pre_smooth4)
#pre_smooth_words5 = convert_to_words(pre_smooth5)
print pre_smooth_words

``````
``````

[7 7 7 0 0 0 5 5 0 5 0 5 5 0 5 7 5 0 6 4 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 4 4
4 4 4 4]
['OTHER', 'OTHER', 'OTHER', 'your_mount', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'OTHER', 'opponent_closed_guard', 'your_mount', 'opponent_back_control', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc']

``````
``````

In [111]:

n_components = 8 # ('ybc', 'ymount', 'ysc', 'ycg', 'ocg', 'osc_mount', 'obc', 'other')
# n_components = 3
startprob = np.array([0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.65,]) # users will probably turn on sensor standing
# startprob = np.array([0.34, 0.33, 0.33])

``````
``````

In [112]:

# transmat = np.array([[0.34, 0.33, 0.33], [0.9, 0.05, 0.05], [0.9, 0.05, 0.05]])

"""
probability of these positions given current state:

your_mount' if v == 0
else 'your_side_control' if v == 1
else 'your_closed_guard' if v == 2
else 'your_back_control' if v == 3
else 'opponent_mount_or_sc' if v == 4
else 'opponent_closed_guard' if v == 5
else 'opponent_back_control' if v == 6
else 'OTHER' if v == 7

transition_probability = {
'ymt' : {'ymount': 0.800, 'ysc': 0.050, 'ycg': 0.010, 'ybc': 0.050, 'osc_mount': 0.001, 'ocg': 0.050, 'obc': 0.001, 'other': 0.038},
'ysc' : {'ymount': 0.100, 'ysc': 0.800, 'ycg': 0.010, 'ybc': 0.010, 'osc_mount': 0.001, 'ocg': 0.050, 'obc': 0.001, 'other': 0.028},
'ycg' : {'ymount': 0.010, 'ysc': 0.050, 'ycg': 0.800, 'ybc': 0.010, 'osc_mount': 0.050, 'ocg': 0.001, 'obc': 0.001, 'other': 0.078},
'ybc' : {'ymount': 0.050, 'ysc': 0.010, 'ycg': 0.050, 'ybc': 0.800, 'osc_mount': 0.001, 'ocg': 0.010, 'obc': 0.001, 'other': 0.078},
'omt' : {'ymount': 0.001, 'ysc': 0.050, 'ycg': 0.010, 'ybc': 0.001, 'osc_mount': 0.800, 'ocg': 0.050, 'obc': 0.050, 'other': 0.038},
'ocg' : {'ymount': 0.100, 'ysc': 0.050, 'ycg': 0.010, 'ybc': 0.010, 'osc_mount': 0.001, 'ocg': 0.800, 'obc': 0.001, 'other': 0.028},
'obc' : {'ymount': 0.010, 'ysc': 0.050, 'ycg': 0.001, 'ybc': 0.010, 'osc_mount': 0.050, 'ocg': 0.001, 'obc': 0.800, 'other': 0.078},
'oth' : {'ymount': 0.050, 'ysc': 0.010, 'ycg': 0.050, 'ybc': 0.078, 'osc_mount': 0.001, 'ocg': 0.010, 'obc': 0.001, 'other': 0.800}
}
"""

transmat = np.array([
[0.800, 0.050, 0.010, 0.050, 0.001, 0.050, 0.001, 0.038],
[0.100, 0.800, 0.010, 0.010, 0.001, 0.050, 0.001, 0.028],
[0.010, 0.050, 0.800, 0.010, 0.050, 0.001, 0.001, 0.078],
[0.050, 0.010, 0.050, 0.800, 0.001, 0.010, 0.001, 0.078],
[0.001, 0.050, 0.010, 0.001, 0.800, 0.050, 0.050, 0.038],
[0.100, 0.050, 0.010, 0.010, 0.001, 0.800, 0.001, 0.028],
[0.010, 0.050, 0.001, 0.010, 0.050, 0.001, 0.800, 0.078],
[0.050, 0.010, 0.050, 0.078, 0.001, 0.010, 0.001, 0.800],
])

``````
``````

In [113]:

# emissionprob = np.array([[0.34, 0.33, 0.33], [0.4, 0.55, 0.05], [0.05, 0.55, 0.4]])

"""

probability of these positions given current state:

your_mount' if v == 0
else 'your_side_control' if v == 1
else 'your_closed_guard' if v == 2
else 'your_back_control' if v == 3
else 'opponent_mount_or_sc' if v == 4
else 'opponent_closed_guard' if v == 5
else 'opponent_back_control' if v == 6
else 'OTHER' if v == 7

emission_probability = {
'ymt' : {'ymount': 0.500, 'ysc': 0.050, 'ycg': 0.010, 'ybc': 0.050, 'osc_mount': 0.001, 'ocg': 0.350, 'obc': 0.001, 'other': 0.038},
'ysc' : {'ymount': 0.100, 'ysc': 0.800, 'ycg': 0.010, 'ybc': 0.010, 'osc_mount': 0.001, 'ocg': 0.050, 'obc': 0.001, 'other': 0.028},
'ycg' : {'ymount': 0.010, 'ysc': 0.050, 'ycg': 0.400, 'ybc': 0.010, 'osc_mount': 0.500, 'ocg': 0.001, 'obc': 0.001, 'other': 0.078},
'ybc' : {'ymount': 0.050, 'ysc': 0.010, 'ycg': 0.050, 'ybc': 0.600, 'osc_mount': 0.001, 'ocg': 0.010, 'obc': 0.201, 'other': 0.078},
'omt' : {'ymount': 0.001, 'ysc': 0.050, 'ycg': 0.210, 'ybc': 0.050, 'osc_mount': 0.600, 'ocg': 0.050, 'obc': 0.001, 'other': 0.038},
'ocg' : {'ymount': 0.400, 'ysc': 0.050, 'ycg': 0.010, 'ybc': 0.010, 'osc_mount': 0.001, 'ocg': 0.400, 'obc': 0.001, 'other': 0.028},
'obc' : {'ymount': 0.010, 'ysc': 0.050, 'ycg': 0.001, 'ybc': 0.110, 'osc_mount': 0.050, 'ocg': 0.001, 'obc': 0.700, 'other': 0.078},
'oth' : {'ymount': 0.050, 'ysc': 0.010, 'ycg': 0.050, 'ybc': 0.078, 'osc_mount': 0.001, 'ocg': 0.010, 'obc': 0.001, 'other': 0.800}
}
"""

emissionprob = np.array([
[0.500, 0.050, 0.010, 0.050, 0.001, 0.350, 0.001, 0.038],
[0.100, 0.800, 0.010, 0.010, 0.001, 0.050, 0.001, 0.028],
[0.010, 0.050, 0.350, 0.010, 0.500, 0.001, 0.001, 0.078],
[0.050, 0.010, 0.050, 0.700, 0.001, 0.010, 0.101, 0.078],
[0.001, 0.050, 0.210, 0.050, 0.600, 0.050, 0.001, 0.038],
[0.400, 0.050, 0.010, 0.010, 0.001, 0.400, 0.001, 0.028],
[0.010, 0.050, 0.001, 0.110, 0.050, 0.001, 0.700, 0.078],
[0.050, 0.010, 0.050, 0.078, 0.001, 0.010, 0.001, 0.800],
])

``````
``````

In [114]:

# Hidden Markov Model with multinomial (discrete) emissions
model = hmm.MultinomialHMM(n_components=n_components,
n_iter=10,
verbose=False)

model.startprob_ = startprob
model.transmat_ = transmat
model.emissionprob_ = emissionprob
# model.n_features = 8

``````
``````

In [115]:

# observations = np.array([1, 1, 2, 2, 1, 0, 1, 2, 2, 0])
observations = np.array(pre_smooth)
observations2 = np.array(pre_smooth2)
#observations3 = np.array(pre_smooth3)
#observations4 = np.array(pre_smooth4)
#observations5 = np.array(pre_smooth5)
a,b = model.sample(5)
print a,b
print '=========='

n_samples = len(observations)
data = observations.reshape((n_samples, -1))
print data

n_samples2 = len(observations2)
data2 = observations2.reshape((n_samples2, -1))

#n_samples3 = len(observations3)
#data3 = observations3.reshape((n_samples3, -1))

#n_samples4 = len(observations4)
#data4 = observations4.reshape((n_samples4, -1))

#n_samples5 = len(observations5)
#data5 = observations5.reshape((n_samples5, -1))

``````
``````

[[2]
[2]
[4]
[4]
[3]] [4 4 4 4 4]
==========
[[7]
[7]
[7]
[0]
[0]
[0]
[5]
[5]
[0]
[5]
[0]
[5]
[5]
[0]
[5]
[7]
[5]
[0]
[6]
[4]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
[2]
[4]
[2]
[2]
[2]
[2]
[2]
[2]
[4]
[4]
[4]
[4]
[4]
[4]]

``````
``````

In [116]:

# decode(X, lengths=None, algorithm=None)[source]
# Find most likely state sequence corresponding to X.
# Will work best for organic tests

"""correct sequence
your_mount' if v == 0
else 'your_side_control' if v == 1
else 'your_closed_guard' if v == 2
else 'your_back_control' if v == 3
else 'opponent_mount_or_sc' if v == 4
else 'opponent_closed_guard' if v == 5
else 'opponent_back_control' if v == 6
else 'OTHER' if v == 7

[3, 0, 1, 2, 5, 4, 6]

"""

``````
``````

Out[116]:

"correct sequence\nyour_mount' if v == 0 \nelse 'your_side_control' if v == 1\nelse 'your_closed_guard' if v == 2\nelse 'your_back_control' if v == 3\nelse 'opponent_mount_or_sc' if v == 4\nelse 'opponent_closed_guard' if v == 5\nelse 'opponent_back_control' if v == 6\nelse 'OTHER' if v == 7\n\n\n[3, 0, 1, 2, 5, 4, 6]\n\n"

``````
``````

In [117]:

print 'TEST 1'

result = model.decode(data, algorithm='viterbi')
print 'pre smooth: {}'.format(pre_smooth)
print 'result accuracy {}%'.format(result[0])
print 'final result: {}'.format(result[1])

result_words = convert_to_words(result[1])
print '====================='
print 'pre smooth words: {}'.format(pre_smooth_words)
print '====================='
print 'result words: {}'.format(result_words)

print '\n'
print "pre smooth stats"
print get_position_stats(pre_smooth_words)

print '\n'

print 'result stats'
print get_position_stats(result_words)

print '******************'

``````
``````

TEST 1
pre smooth: [7 7 7 0 0 0 5 5 0 5 0 5 5 0 5 7 5 0 6 4 2 2 2 2 2 2 2 2 4 2 2 2 2 2 2 4 4
4 4 4 4]
result accuracy -56.3499979936%
final result: [7 7 7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2]
=====================
pre smooth words: ['OTHER', 'OTHER', 'OTHER', 'your_mount', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'OTHER', 'opponent_closed_guard', 'your_mount', 'opponent_back_control', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc']
=====================
result words: ['OTHER', 'OTHER', 'OTHER', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_back_control', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard']

pre smooth stats
Opponent Mount or Opponent Side Control: 0.19512195122
Opponent Closed Guard: 0.170731707317
Opponent Back Control: 0.0243902439024
OTHER: 0.0975609756098

None

result stats
Opponent Mount or Opponent Side Control: 0.0
Opponent Closed Guard: 0.0
Opponent Back Control: 0.0
OTHER: 0.0731707317073

None
******************

``````
``````

In [118]:

print 'TEST2'
result2 = model.decode(data2, algorithm='viterbi')
print 'pre smooth: {}'.format(pre_smooth2)
print 'result accuracy {}%'.format(result2[0])
print 'final result: {}'.format(result2[1])

result_words2 = convert_to_words(result2[1])
print '====================='
print 'pre smooth words: {}'.format(pre_smooth_words2)
print '====================='
print 'result words: {}'.format(result_words2)

print '\n'
print "pre smooth stats"
print get_position_stats(pre_smooth_words2)

print '\n'

print 'result stats'
print get_position_stats(result_words2)

print '******************'

``````
``````

TEST2
pre smooth: [7 7 3 6 3 3 3 3 3 0 0 0 0 5 7 0 0 1 1 0 1 6 1 1 1 1 0 7 4 4 4 4 2 3 4 4 2
2 2 2 2 4 2 5 0 0 5 5 5 5 0 5 5 5 2 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 4 6 6 6
6 6 6 6 6 1 0]
result accuracy -120.080821923%
final result: [7 7 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 7 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 2 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6
6 6 6 6 6 1 1]
=====================
pre smooth words: ['OTHER', 'OTHER', 'your_back_control', 'opponent_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'opponent_closed_guard', 'OTHER', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_mount', 'your_side_control', 'opponent_back_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'your_back_control', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_closed_guard', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'your_mount']
=====================
result words: ['OTHER', 'OTHER', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'your_side_control']

pre smooth stats
Opponent Mount or Opponent Side Control: 0.259259259259
Opponent Closed Guard: 0.111111111111
Opponent Back Control: 0.148148148148
OTHER: 0.0493827160494

None

result stats
Opponent Mount or Opponent Side Control: 0.16049382716
Opponent Closed Guard: 0.0
Opponent Back Control: 0.135802469136
OTHER: 0.037037037037

None
******************

``````
``````

In [21]:

"""
print 'TEST3'
result3 = model.decode(data3, algorithm='viterbi')
print 'pre smooth: {}'.format(pre_smooth3)
print 'result accuracy {}%'.format(result3[0])
print 'final result: {}'.format(result3[1])

result_words3 = convert_to_words(result3[1])
print '====================='
print 'pre smooth words: {}'.format(pre_smooth_words3)
print '====================='
print 'result words: {}'.format(result_words3)

print '\n'
print "pre smooth stats"
print get_position_stats(pre_smooth_words3)

print '\n'

print 'result stats'
print get_position_stats(result_words3)

print '******************'
"""

``````
``````

TEST3
pre smooth: [7 7 6 1 6 6 1 6 6 6 3 3 3 1 0 5 0 0 0 0 1 1 1 1 1 1 1 0 4 2 2 2 2 2 2 2 2
2 2 2 7 0 5 5 0 5 5 5 5 5 5 5 5 7 4 4 4 4 2 4 2 4 4 4 2 4 4 4 4 2 4 3 6 2
4 6 3 4 6 6 6 6 6 6 6 6 6 7 5 7]
result accuracy -142.448382567%
final result: [7 7 6 6 6 6 6 6 6 6 3 3 3 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2
2 2 2 7 0 0 0 0 0 0 0 0 0 0 0 0 7 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 6 6 4
4 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7]
=====================
pre smooth words: ['OTHER', 'OTHER', 'opponent_back_control', 'your_side_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_side_control', 'your_mount', 'opponent_closed_guard', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'your_back_control', 'opponent_back_control', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_back_control', 'your_back_control', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'OTHER', 'opponent_closed_guard', 'OTHER']
=====================
result words: ['OTHER', 'OTHER', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'OTHER', 'OTHER', 'OTHER']

pre smooth stats
Opponent Mount or Opponent Side Control: 0.177777777778
Opponent Closed Guard: 0.133333333333
Opponent Back Control: 0.188888888889
OTHER: 0.0666666666667

None

result stats
Opponent Mount or Opponent Side Control: 0.133333333333
Opponent Closed Guard: 0.0
Opponent Back Control: 0.244444444444
OTHER: 0.0777777777778

None
******************

``````
``````

In [22]:

"""
print 'TEST4'
result4 = model.decode(data4, algorithm='viterbi')
print 'pre smooth: {}'.format(pre_smooth4)
print 'result accuracy {}%'.format(result4[0])
print 'final result: {}'.format(result4[1])

result_words4 = convert_to_words(result4[1])
print '====================='
print 'pre smooth words: {}'.format(pre_smooth_words4)
print '====================='
print 'result words: {}'.format(result_words4)

print '\n'
print "pre smooth stats"
print get_position_stats(pre_smooth_words4)

print '\n'

print 'result stats'
print get_position_stats(result_words4)

print '******************'
"""

``````
``````

TEST4
pre smooth: [7 7 7 6 3 3 6 6 6 3 3 3 5 0 0 5 5 5 5 5 5 7 7 7 7 5 7 5 0 1 1 1 1 1 1 1 1
1 1 1 5 7 2 2 2 2 7 7 7 2 2 2 7 7 7 2 2 7 7 7 7 2 2 2 5 5 5 5 5 5 5 5 5 5
5 5 5 5 5 0 5 7 4 4 4 4 4 7 7 7 7 7 7 7 7 7 2 4 4 4 4 4 4 4 4 7 4 7 7 7 7
4 4 2 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 0 7 5]
result accuracy -190.670795411%
final result: [7 7 7 3 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 0 7 7 7 7 0 0 0 0 1 1 1 1 1 1 1 1
1 1 1 0 7 2 2 2 2 7 7 7 2 2 2 7 7 7 7 7 7 7 7 7 2 2 2 0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 7 2 2 2 2 2 7 7 7 7 7 7 7 7 7 2 2 2 2 2 2 2 2 2 2 2 7 7 7 7
2 4 4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 0 0 0 0]
=====================
pre smooth words: ['OTHER', 'OTHER', 'OTHER', 'opponent_back_control', 'your_back_control', 'your_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'opponent_closed_guard', 'your_mount', 'your_mount', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'opponent_closed_guard', 'OTHER', 'opponent_closed_guard', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'opponent_closed_guard', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'opponent_closed_guard', 'your_mount', 'opponent_closed_guard', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'OTHER', 'opponent_mount_or_sc', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_mount', 'your_mount', 'OTHER', 'opponent_closed_guard']
=====================
result words: ['OTHER', 'OTHER', 'OTHER', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'OTHER', 'OTHER', 'OTHER', 'OTHER', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount']

pre smooth stats
Opponent Mount or Opponent Side Control: 0.118518518519
Opponent Closed Guard: 0.2
Opponent Back Control: 0.155555555556
OTHER: 0.259259259259

None

result stats
Opponent Mount or Opponent Side Control: 0.0148148148148
Opponent Closed Guard: 0.0
Opponent Back Control: 0.125925925926
OTHER: 0.251851851852

None
******************

``````
``````

In [23]:

"""
print 'TEST5'
result5 = model.decode(data5, algorithm='viterbi')
print 'pre smooth: {}'.format(pre_smooth5)
print 'result accuracy {}%'.format(result5[0])
print 'final result: {}'.format(result5[1])

result_words5 = convert_to_words(result5[1])
print '====================='
print 'pre smooth words: {}'.format(pre_smooth_words5)
print '====================='
print 'result words: {}'.format(result_words5)

print '\n'
print "pre smooth stats"
print get_position_stats(pre_smooth_words5)

print '\n'

print 'result stats'
print get_position_stats(result_words5)
"""

``````
``````

TEST5
pre smooth: [7 7 2 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0 4 4 4 4 4 4 2 2 4 4
1 0 0 0 3 0 0 0 5 0 4 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 6 3 6 6 6 6 6 6 6 6 6
1 1 5 7]
result accuracy -101.599747597%
final result: [7 7 3 3 3 3 3 3 3 3 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2
1 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 6 6 6
1 1 0 0]
=====================
pre smooth words: ['OTHER', 'OTHER', 'your_closed_guard', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_mount', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_side_control', 'your_mount', 'your_mount', 'your_mount', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'opponent_closed_guard', 'your_mount', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'your_closed_guard', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'your_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'your_side_control', 'opponent_closed_guard', 'OTHER']
=====================
result words: ['OTHER', 'OTHER', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_back_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_side_control', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_closed_guard', 'your_side_control', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'your_mount', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_mount_or_sc', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'opponent_back_control', 'your_side_control', 'your_side_control', 'your_mount', 'your_mount']

pre smooth stats
Opponent Mount or Opponent Side Control: 0.294871794872
Opponent Closed Guard: 0.025641025641
Opponent Back Control: 0.128205128205
OTHER: 0.0384615384615

None

result stats
Opponent Mount or Opponent Side Control: 0.205128205128
Opponent Closed Guard: 0.0
Opponent Back Control: 0.141025641026
OTHER: 0.025641025641

None

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