This file takes as input a sequence of data from the CNN, and attempts to refine that into a more accurate output command.
Some credit belongs to https://github.com/harvitronix/five-video-classification-methods/blob/master/models.py for providing inspiration.
In [1]:
#Create references to important directories we will use over and over
import os, sys
DATA_HOME_DIR = '/home/nathan/olin/spring2017/line-follower/line-follower/data'
In [2]:
#import modules
import numpy as np
from glob import glob
from PIL import Image
from tqdm import tqdm
import bcolz
from matplotlib import pyplot as plt
import seaborn as sns
%matplotlib inline
In [3]:
from keras.layers import *
from keras.layers.recurrent import LSTM
from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers.wrappers import TimeDistributed
from keras.metrics import categorical_crossentropy, categorical_accuracy
Using TensorFlow backend.
In [4]:
%cd $DATA_HOME_DIR
path = DATA_HOME_DIR
train_path1=path + '/sun_apr_16_office_full_line_1'
train_path2=path + '/qea_blob_1'
# valid_path1=path + '/sun_apr_16_office_full_line_2'
# valid_path2=path + '/qea_blob_2'
valid_path1=path + '/qea-square_3'#+ '/sun_apr_16_office_full_line_2'
# train_path=path + '/qea_blob_1'
# valid_path=path + '/qea_blob_2'
/home/nathan/olin/spring2017/line-follower/line-follower/data
In [5]:
INPUT_LEN = 512 # The number of columns in the CSV
WINDOW_SIZE = 16
In [6]:
def load_array(fname):
return bcolz.open(fname)[:]
def windows(X, Y, seq_len=10):
assert len(X) == len(Y)
result = []
for index in range(X.shape[0] - seq_len):
result.append([X[index:index+seq_len], Y[index+seq_len-1]])
result = np.array(result)
# np.random.shuffle(result)
return np.array(list(result[:,0])), np.array(list(result[:,1]))
def get_data(paths):
Y_return = []
for path in paths:
%cd $path
Y_train = np.genfromtxt('cmd_vel.csv', delimiter=',')[:,1] # only use turning angle
Y_train = np.concatenate((Y_train, Y_train*-1))
Y_return.extend(Y_train)
X_all = load_array(paths[-1]+'/X_train_features3.b')
X_all = np.reshape(X_all, (len(X_all), INPUT_LEN))
print (len(X_all), len(Y_return))
X_windowed, Y_windowed = windows(X_all, Y_return, WINDOW_SIZE)
return np.array(X_windowed), np.array(Y_windowed)
In [7]:
X_train, Y_train = get_data([train_path1, train_path2])
X_valid, Y_valid = get_data([valid_path1])
/home/nathan/olin/spring2017/line-follower/line-follower/data/sun_apr_16_office_full_line_1
/home/nathan/olin/spring2017/line-follower/line-follower/data/qea_blob_1
1312 1312
/home/nathan/olin/spring2017/line-follower/line-follower/data/qea-square_3
190 190
In [8]:
X_train.shape
Out[8]:
(1296, 16, 512)
In [9]:
in_shape = (WINDOW_SIZE, INPUT_LEN)
In [10]:
def get_model():
model = Sequential([
LSTM(2048, return_sequences=False, input_shape=in_shape),
Dropout(0.5),
# Flatten(input_shape=in_shape),
Dense(512, activation='relu'),
# Dense(512, activation='relu'),
# Dense(512, activation='relu'),
Dropout(0.5),
Dense(1)
])
model.compile(loss='mean_absolute_error', optimizer='adam')
return model
model = get_model()
model.summary()
____________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
====================================================================================================
lstm_1 (LSTM) (None, 2048) 20979712 lstm_input_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout) (None, 2048) 0 lstm_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 512) 1049088 dropout_1[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout) (None, 512) 0 dense_1[0][0]
____________________________________________________________________________________________________
dense_2 (Dense) (None, 1) 513 dropout_2[0][0]
====================================================================================================
Total params: 22,029,313
Trainable params: 22,029,313
Non-trainable params: 0
____________________________________________________________________________________________________
In [12]:
%cd $DATA_HOME_DIR
model.load_weights('LSTM_postprocessor_v1.h5')
/home/nathan/olin/spring2017/line-follower/line-follower/data
In [32]:
history = model.fit(X_train, Y_train,#X_train[:,-1],
batch_size = 96,
nb_epoch=150,
validation_data=(X_valid, Y_valid),
verbose=True)
Train on 1296 samples, validate on 578 samples
Epoch 1/150
1296/1296 [==============================] - 1s - loss: 0.0138 - val_loss: 0.0398
Epoch 2/150
1296/1296 [==============================] - 1s - loss: 0.0138 - val_loss: 0.0398
Epoch 3/150
1296/1296 [==============================] - 1s - loss: 0.0138 - val_loss: 0.0391
Epoch 4/150
1296/1296 [==============================] - 1s - loss: 0.0134 - val_loss: 0.0400
Epoch 5/150
1296/1296 [==============================] - 1s - loss: 0.0136 - val_loss: 0.0393
Epoch 6/150
1296/1296 [==============================] - 1s - loss: 0.0127 - val_loss: 0.0390
Epoch 7/150
1296/1296 [==============================] - 1s - loss: 0.0122 - val_loss: 0.0393
Epoch 8/150
1296/1296 [==============================] - 1s - loss: 0.0123 - val_loss: 0.0391
Epoch 9/150
1296/1296 [==============================] - 1s - loss: 0.0122 - val_loss: 0.0385
Epoch 10/150
1296/1296 [==============================] - 1s - loss: 0.0117 - val_loss: 0.0393
Epoch 11/150
1296/1296 [==============================] - 1s - loss: 0.0115 - val_loss: 0.0387
Epoch 12/150
1296/1296 [==============================] - 1s - loss: 0.0120 - val_loss: 0.0384
Epoch 13/150
1296/1296 [==============================] - 1s - loss: 0.0112 - val_loss: 0.0394
Epoch 14/150
1296/1296 [==============================] - 1s - loss: 0.0119 - val_loss: 0.0388
Epoch 15/150
1296/1296 [==============================] - 1s - loss: 0.0121 - val_loss: 0.0393
Epoch 16/150
1296/1296 [==============================] - 1s - loss: 0.0107 - val_loss: 0.0388
Epoch 17/150
1296/1296 [==============================] - 1s - loss: 0.0109 - val_loss: 0.0388
Epoch 18/150
1296/1296 [==============================] - 1s - loss: 0.0110 - val_loss: 0.0386
Epoch 19/150
1296/1296 [==============================] - 1s - loss: 0.0106 - val_loss: 0.0387
Epoch 20/150
1296/1296 [==============================] - 1s - loss: 0.0105 - val_loss: 0.0382
Epoch 21/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0386
Epoch 22/150
1296/1296 [==============================] - 1s - loss: 0.0103 - val_loss: 0.0398
Epoch 23/150
1296/1296 [==============================] - 1s - loss: 0.0106 - val_loss: 0.0396
Epoch 24/150
1296/1296 [==============================] - 1s - loss: 0.0101 - val_loss: 0.0390
Epoch 25/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0395
Epoch 26/150
1296/1296 [==============================] - 1s - loss: 0.0096 - val_loss: 0.0384
Epoch 27/150
1296/1296 [==============================] - 1s - loss: 0.0100 - val_loss: 0.0387
Epoch 28/150
1296/1296 [==============================] - 1s - loss: 0.0097 - val_loss: 0.0387
Epoch 29/150
1296/1296 [==============================] - 1s - loss: 0.0099 - val_loss: 0.0389
Epoch 30/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0392
Epoch 31/150
1296/1296 [==============================] - 1s - loss: 0.0101 - val_loss: 0.0396
Epoch 32/150
1296/1296 [==============================] - 1s - loss: 0.0096 - val_loss: 0.0390
Epoch 33/150
1296/1296 [==============================] - 1s - loss: 0.0093 - val_loss: 0.0396
Epoch 34/150
1296/1296 [==============================] - 1s - loss: 0.0098 - val_loss: 0.0386
Epoch 35/150
1296/1296 [==============================] - 1s - loss: 0.0095 - val_loss: 0.0387
Epoch 36/150
1296/1296 [==============================] - 1s - loss: 0.0095 - val_loss: 0.0387
Epoch 37/150
1296/1296 [==============================] - 1s - loss: 0.0102 - val_loss: 0.0392
Epoch 38/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0388
Epoch 39/150
1296/1296 [==============================] - 1s - loss: 0.0096 - val_loss: 0.0401
Epoch 40/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0385
Epoch 41/150
1296/1296 [==============================] - 1s - loss: 0.0099 - val_loss: 0.0391
Epoch 42/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0398
Epoch 43/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0388
Epoch 44/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0393
Epoch 45/150
1296/1296 [==============================] - 1s - loss: 0.0094 - val_loss: 0.0393
Epoch 46/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0387
Epoch 47/150
1296/1296 [==============================] - 1s - loss: 0.0094 - val_loss: 0.0395
Epoch 48/150
1296/1296 [==============================] - 1s - loss: 0.0092 - val_loss: 0.0389
Epoch 49/150
1296/1296 [==============================] - 1s - loss: 0.0095 - val_loss: 0.0385
Epoch 50/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0388
Epoch 51/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0388
Epoch 52/150
1296/1296 [==============================] - 1s - loss: 0.0092 - val_loss: 0.0393
Epoch 53/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0391
Epoch 54/150
1296/1296 [==============================] - 1s - loss: 0.0093 - val_loss: 0.0397
Epoch 55/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0386
Epoch 56/150
1296/1296 [==============================] - 1s - loss: 0.0089 - val_loss: 0.0392
Epoch 57/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0394
Epoch 58/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0390
Epoch 59/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0386
Epoch 60/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0401
Epoch 61/150
1296/1296 [==============================] - 1s - loss: 0.0089 - val_loss: 0.0389
Epoch 62/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0391
Epoch 63/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0399
Epoch 64/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0385
Epoch 65/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0395
Epoch 66/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0388
Epoch 67/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0394
Epoch 68/150
1296/1296 [==============================] - 1s - loss: 0.0093 - val_loss: 0.0396
Epoch 69/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0386
Epoch 70/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0391
Epoch 71/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0389
Epoch 72/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0395
Epoch 73/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0392
Epoch 74/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0385
Epoch 75/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0387
Epoch 76/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0391
Epoch 77/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0398
Epoch 78/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0392
Epoch 79/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0387
Epoch 80/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0388
Epoch 81/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0390
Epoch 82/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0394
Epoch 83/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0392
Epoch 84/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0394
Epoch 85/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0387
Epoch 86/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0392
Epoch 87/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0387
Epoch 88/150
1296/1296 [==============================] - 1s - loss: 0.0090 - val_loss: 0.0390
Epoch 89/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0388
Epoch 90/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0388
Epoch 91/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0397
Epoch 92/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0394
Epoch 93/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0392
Epoch 94/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0393
Epoch 95/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0401
Epoch 96/150
1296/1296 [==============================] - 1s - loss: 0.0092 - val_loss: 0.0392
Epoch 97/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0400
Epoch 98/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0388
Epoch 99/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0394
Epoch 100/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0389
Epoch 101/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0395
Epoch 102/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0389
Epoch 103/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0394
Epoch 104/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0395
Epoch 105/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0395
Epoch 106/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0390
Epoch 107/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0397
Epoch 108/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0400
Epoch 109/150
1296/1296 [==============================] - 1s - loss: 0.0077 - val_loss: 0.0391
Epoch 110/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0401
Epoch 111/150
1296/1296 [==============================] - 1s - loss: 0.0088 - val_loss: 0.0388
Epoch 112/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0392
Epoch 113/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0391
Epoch 114/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0390
Epoch 115/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0395
Epoch 116/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0395
Epoch 117/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0394
Epoch 118/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0388
Epoch 119/150
1296/1296 [==============================] - 1s - loss: 0.0089 - val_loss: 0.0398
Epoch 120/150
1296/1296 [==============================] - 1s - loss: 0.0084 - val_loss: 0.0390
Epoch 121/150
1296/1296 [==============================] - 1s - loss: 0.0097 - val_loss: 0.0397
Epoch 122/150
1296/1296 [==============================] - 1s - loss: 0.0091 - val_loss: 0.0391
Epoch 123/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0387
Epoch 124/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0404
Epoch 125/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0391
Epoch 126/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0391
Epoch 127/150
1296/1296 [==============================] - 1s - loss: 0.0076 - val_loss: 0.0391
Epoch 128/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0390
Epoch 129/150
1296/1296 [==============================] - 1s - loss: 0.0087 - val_loss: 0.0390
Epoch 130/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0393
Epoch 131/150
1296/1296 [==============================] - 1s - loss: 0.0085 - val_loss: 0.0394
Epoch 132/150
1296/1296 [==============================] - 1s - loss: 0.0086 - val_loss: 0.0391
Epoch 133/150
1296/1296 [==============================] - 1s - loss: 0.0075 - val_loss: 0.0391
Epoch 134/150
1296/1296 [==============================] - 1s - loss: 0.0080 - val_loss: 0.0389
Epoch 135/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0401
Epoch 136/150
1296/1296 [==============================] - 1s - loss: 0.0076 - val_loss: 0.0400
Epoch 137/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0389
Epoch 138/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0396
Epoch 139/150
1296/1296 [==============================] - 1s - loss: 0.0077 - val_loss: 0.0389
Epoch 140/150
1296/1296 [==============================] - 1s - loss: 0.0081 - val_loss: 0.0386
Epoch 141/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0393
Epoch 142/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0398
Epoch 143/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0392
Epoch 144/150
1296/1296 [==============================] - 1s - loss: 0.0076 - val_loss: 0.0391
Epoch 145/150
1296/1296 [==============================] - 1s - loss: 0.0082 - val_loss: 0.0389
Epoch 146/150
1296/1296 [==============================] - 1s - loss: 0.0078 - val_loss: 0.0389
Epoch 147/150
1296/1296 [==============================] - 1s - loss: 0.0083 - val_loss: 0.0393
Epoch 148/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0394
Epoch 149/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0391
Epoch 150/150
1296/1296 [==============================] - 1s - loss: 0.0079 - val_loss: 0.0391
In [13]:
conv_predictions = X_train[:,-1]
recurrent_predictions = model.predict(X_train)
ground_truth = Y_train
for x,y,z in zip(conv_predictions[:,0], ground_truth, recurrent_predictions[:,0]):
print ("{:07f}\t{:07f}\t{:07f}\t".format(x,y,z))
0.000000 0.009191 -0.001004
0.000000 -0.000000 -0.000995
0.000000 -0.000000 -0.000988
0.000000 -0.000000 -0.000984
0.000000 -0.000000 -0.000985
0.000000 -0.000000 -0.000986
0.000000 -0.084054 -0.092041
0.000000 -0.000000 -0.001812
0.000000 -0.000000 -0.001331
0.000000 -0.000000 -0.001100
0.000000 -0.000000 -0.001096
0.000000 -0.000000 -0.001089
0.000000 -0.000000 -0.001059
0.000000 0.064105 0.063707
0.000000 0.036648 0.032237
0.000000 0.006695 -0.000578
0.000000 0.046632 0.044549
0.000000 0.076585 0.073129
0.000000 -0.000000 -0.000434
0.000000 -0.000000 -0.000453
0.000000 0.031656 0.035124
0.000000 0.061609 0.038705
0.000000 0.096564 0.094046
0.000000 0.049128 0.046650
0.000000 0.026663 0.027809
0.000000 0.153974 0.090551
0.000000 -0.000000 -0.000555
0.000000 0.069097 0.068128
0.000000 0.079081 0.081587
0.000000 0.054120 0.055685
0.000000 0.049128 0.047165
0.000000 0.049128 0.046470
0.000000 0.079081 0.078815
0.000000 0.081577 0.080389
0.000000 0.026663 0.030959
0.000000 0.036648 0.036661
0.000000 0.106548 0.104779
0.000000 0.106548 0.109401
0.000000 0.151478 0.139595
0.000000 -0.000000 0.000471
0.000000 0.079081 0.071040
0.000000 -0.000000 0.000411
0.000000 0.009191 -0.000061
0.000000 -0.000000 -0.000369
0.000000 -0.000000 -0.000689
0.000000 -0.000000 -0.000756
0.000000 -0.000000 -0.000786
0.000000 0.064105 0.056766
0.000000 -0.000000 -0.000879
0.000000 -0.000000 -0.000727
0.000000 -0.000000 -0.000717
0.000000 -0.000000 -0.000807
0.000000 -0.000000 -0.000902
0.000000 -0.000000 -0.000918
0.000000 -0.000000 -0.000916
0.000000 -0.124001 -0.123337
0.000000 -0.000000 -0.002002
0.000000 -0.000000 -0.001602
0.000000 -0.000000 -0.001401
0.000000 -0.000000 -0.001258
0.000000 -0.000000 -0.001147
0.000000 -0.064085 -0.060780
0.000000 -0.076566 -0.074029
0.000000 -0.076566 -0.073980
0.000000 -0.300000 -0.297200
0.000000 -0.000000 -0.004188
0.000000 -0.000000 -0.002408
0.000000 -0.096544 -0.102958
0.000000 -0.300000 -0.299973
0.000000 -0.000000 -0.005478
0.000000 -0.000000 -0.002880
0.000000 -0.000000 -0.003334
0.000000 -0.086550 -0.087185
0.000000 -0.096544 -0.094853
0.000000 -0.096544 -0.097950
0.000000 -0.096544 -0.096982
0.000000 -0.096544 -0.098060
0.000000 -0.064085 -0.070437
0.000000 -0.024148 -0.006981
0.000000 -0.300000 -0.291100
0.000000 -0.000000 -0.005527
0.000000 -0.000000 -0.002598
0.000000 -0.000000 -0.003021
0.000000 -0.000000 -0.002801
0.000000 -0.000000 -0.001976
0.000000 -0.000000 -0.001365
0.000000 -0.000000 -0.001378
0.000000 -0.000000 -0.001427
0.000000 -0.000000 -0.001348
0.000000 -0.000000 -0.001162
0.000000 -0.000000 -0.001033
0.000000 -0.076566 -0.078611
0.000000 -0.051605 -0.056751
0.000000 -0.049109 -0.051700
0.000000 -0.046613 -0.050938
0.000000 -0.001683 -0.002850
0.000000 -0.059093 -0.064078
0.000000 -0.000000 -0.002542
0.000000 -0.000000 -0.001679
0.000000 -0.000000 -0.001269
0.000000 -0.000000 -0.001149
0.000000 -0.000000 -0.001077
0.000000 -0.000000 -0.001027
0.000000 -0.000000 -0.000993
0.000000 -0.000000 -0.000966
0.000000 -0.000000 -0.000966
0.000000 -0.000000 -0.000977
0.000000 -0.000000 -0.000993
0.000000 -0.011668 -0.001277
0.000000 -0.000000 -0.001007
0.000000 -0.000000 -0.000994
0.000000 -0.046613 -0.004067
0.000000 -0.049109 -0.051053
0.000000 -0.000000 -0.001219
0.000000 -0.000000 -0.001099
0.000000 -0.000000 -0.001136
0.000000 -0.000000 -0.001161
0.000000 -0.000000 -0.001074
0.000000 -0.000000 -0.001002
0.000000 -0.148962 -0.147747
0.000000 -0.000000 -0.002891
0.000000 0.258819 0.228237
0.000000 -0.000000 -0.000295
0.000000 -0.176419 -0.176108
0.000000 -0.000000 -0.001426
0.000000 -0.036628 -0.039076
0.000000 -0.061589 -0.064306
0.000000 -0.116513 -0.123968
0.000000 -0.000000 -0.002499
0.000000 -0.000000 -0.001683
0.000000 -0.000000 -0.001348
0.000000 -0.044117 -0.048669
0.000000 -0.026644 -0.029642
0.000000 -0.049109 -0.050939
0.000000 -0.000000 -0.001750
0.000000 -0.000000 -0.001320
0.000000 -0.069078 -0.076754
0.000000 -0.000000 -0.001735
0.000000 -0.000000 -0.001126
0.000000 -0.000000 -0.001009
0.000000 -0.000000 -0.001051
0.000000 -0.000000 -0.001094
0.000000 -0.000000 -0.001093
0.000000 0.024167 0.027658
0.000000 0.039144 0.040895
0.000000 0.044136 0.044376
0.000000 0.004199 0.000313
0.000000 0.019175 0.011332
0.000000 0.014183 0.010097
0.000000 0.039144 0.035387
0.000000 0.039144 0.043639
0.000000 0.014183 0.012359
0.000000 0.069097 0.065253
0.000000 0.001702 -0.000343
0.000000 -0.001683 -0.000585
0.000000 -0.106529 -0.108536
0.000000 0.046632 0.044947
0.000000 0.004199 -0.001241
0.000000 0.004199 -0.000867
0.000000 0.004199 -0.000830
0.000000 0.004199 -0.000707
0.000000 0.004199 -0.000653
0.000000 0.029159 0.035210
0.000000 0.041640 0.043647
0.000000 0.044136 0.042891
0.000000 -0.000000 -0.000685
0.000000 -0.000000 -0.000625
0.000000 -0.000000 -0.000622
0.000000 -0.000000 -0.000757
0.000000 -0.000000 -0.000858
0.000000 -0.000000 -0.000904
0.000000 -0.000000 -0.000948
0.000000 -0.000000 -0.001000
0.000000 -0.000000 -0.001038
0.000000 -0.039125 -0.040879
0.000000 -0.076566 -0.001161
0.000000 -0.000000 -0.000934
0.000000 0.026663 0.012288
0.000000 -0.000000 -0.000943
0.000000 -0.000000 -0.000852
0.000000 -0.000000 -0.000839
0.000000 -0.000000 -0.000898
0.000000 -0.000000 -0.000984
0.000000 -0.000000 -0.001032
0.000000 -0.000000 -0.000959
0.000000 -0.000000 -0.001005
0.000000 0.011687 0.004044
0.000000 0.016679 0.009546
0.000000 0.026663 0.022904
0.000000 0.031656 0.031471
0.000000 0.061609 0.059688
0.000000 0.009191 0.005020
0.000000 -0.000000 0.000086
0.000000 0.021671 0.018975
0.000000 0.031656 0.031313
0.000000 0.056616 0.052140
0.000000 0.021671 0.015376
0.000000 0.029159 0.031944
0.000000 0.051624 0.049607
0.000000 0.029159 0.030044
0.000000 0.026663 0.024696
0.000000 0.059113 0.055268
0.000000 0.086570 0.088932
0.000000 0.086570 0.086715
0.000000 0.089066 0.086404
0.000000 0.094068 0.090191
0.000000 0.099060 0.099529
0.000000 0.071593 0.070792
0.000000 0.056616 0.064174
0.000000 0.094068 0.094033
0.000000 0.099060 0.087934
0.000000 0.071593 0.068428
0.000000 0.079081 0.085395
0.000000 0.091571 0.093434
0.000000 0.091571 0.090750
0.000000 0.089066 0.084595
0.000000 -0.000000 -0.000333
0.000000 -0.000000 -0.000137
0.000000 -0.000000 -0.000373
0.000000 -0.000000 -0.000593
0.000000 -0.000000 -0.000707
0.000000 0.064105 0.038656
0.000000 0.044136 0.038501
0.000000 0.029159 0.033423
0.000000 -0.000000 -0.000813
0.000000 -0.000000 -0.000680
0.000000 -0.000000 -0.000698
0.000000 -0.000000 -0.000800
0.000000 -0.000000 -0.000854
0.000000 -0.000000 -0.000878
0.000000 -0.000000 -0.000885
0.000000 -0.000000 -0.000944
0.000000 -0.096544 -0.105756
0.000000 -0.059093 -0.064396
0.000000 -0.036628 -0.039562
0.000000 -0.014164 -0.010886
0.000000 -0.084054 -0.078694
0.000000 -0.138978 -0.146965
0.000000 -0.000000 -0.002773
0.000000 -0.106529 -0.108209
0.000000 -0.161443 -0.155370
0.000000 -0.004179 -0.005304
0.000000 -0.071574 -0.071194
0.000000 -0.106529 -0.114392
0.000000 -0.094048 -0.094506
0.000000 -0.074070 -0.084086
0.000000 -0.071574 -0.067969
0.000000 -0.071574 -0.075436
0.000000 -0.036628 -0.037965
0.000000 -0.071574 -0.078076
0.000000 -0.109025 -0.121416
0.000000 -0.046613 -0.045353
0.000000 -0.109025 -0.118679
0.000000 -0.001683 -0.015362
0.000000 -0.049109 -0.054088
0.000000 -0.000000 -0.015322
0.000000 -0.069078 -0.069355
0.000000 -0.109025 -0.107445
0.000000 -0.074070 -0.082892
0.000000 -0.000000 -0.003071
0.000000 -0.000000 -0.002117
0.000000 -0.000000 -0.001537
0.000000 -0.000000 -0.001212
0.000000 -0.041621 -0.048245
0.000000 -0.000000 -0.001301
0.000000 -0.000000 -0.001067
0.000000 -0.000000 -0.001044
0.000000 -0.046613 -0.048502
0.000000 -0.001683 -0.001156
0.000000 -0.000000 -0.000986
0.000000 -0.000000 -0.000970
0.000000 0.000000 -0.000985
0.000000 0.000000 -0.000994
0.000000 0.000000 -0.001008
0.000000 0.000000 -0.001040
0.000000 0.000000 -0.001059
0.000000 0.054101 0.050751
0.000000 0.000000 -0.000957
0.000000 0.000000 -0.000754
0.000000 0.000000 -0.000754
0.000000 0.000000 -0.000845
0.000000 0.000000 -0.000918
0.000000 0.000000 -0.000933
0.000000 0.000000 -0.000940
0.000000 0.000000 -0.000957
0.000000 0.000000 -0.001000
0.000000 -0.009191 -0.001033
0.000000 0.000000 -0.001030
0.000000 0.000000 -0.001005
0.000000 0.000000 -0.000984
0.000000 0.000000 -0.000981
0.000000 0.000000 -0.000995
0.000000 0.084054 0.074888
0.000000 0.000000 -0.000743
0.000000 0.000000 -0.000706
0.000000 0.000000 -0.000690
0.000000 0.000000 -0.000810
0.000000 0.000000 -0.000912
0.000000 0.000000 -0.000933
0.000000 -0.064105 -0.070166
0.000000 -0.036648 -0.001254
0.000000 -0.006695 -0.001122
0.000000 -0.046632 -0.052112
0.000000 -0.076585 -0.078511
0.000000 0.000000 -0.001845
0.000000 0.000000 -0.001257
0.000000 -0.031656 -0.052778
0.000000 -0.061609 -0.055017
0.000000 -0.096564 -0.104987
0.000000 -0.049128 -0.051480
0.000000 -0.026663 -0.029219
0.000000 -0.153974 -0.153795
0.000000 0.000000 -0.004279
0.000000 -0.069097 -0.071664
0.000000 -0.079081 -0.080119
0.000000 -0.054120 -0.052705
0.000000 -0.049128 -0.051304
0.000000 -0.049128 -0.049026
0.000000 -0.079081 -0.080476
0.000000 -0.081577 -0.080247
0.000000 -0.026663 -0.029725
0.000000 -0.036648 -0.043643
0.000000 -0.106548 -0.106577
0.000000 -0.106548 -0.103510
0.000000 -0.151478 -0.153266
0.000000 0.000000 -0.003666
0.000000 -0.079081 -0.079855
0.000000 0.000000 -0.002906
0.000000 -0.009191 -0.002244
0.000000 0.000000 -0.001697
0.000000 0.000000 -0.001401
0.000000 0.000000 -0.001393
0.000000 0.000000 -0.001271
0.000000 -0.064105 -0.069686
0.000000 0.000000 -0.001390
0.000000 0.000000 -0.001095
0.000000 0.000000 -0.001053
0.000000 0.000000 -0.001053
0.000000 0.000000 -0.001037
0.000000 0.000000 -0.001006
0.000000 0.000000 -0.000999
0.000000 0.124001 0.114836
0.000000 0.000000 -0.000752
0.000000 0.000000 -0.000727
0.000000 0.000000 -0.000712
0.000000 0.000000 -0.000707
0.000000 0.000000 -0.000793
0.000000 0.064085 0.061275
0.000000 0.076566 0.074741
0.000000 0.076566 0.073876
0.000000 0.300000 0.266403
0.000000 0.000000 0.001078
0.000000 0.000000 0.000027
0.000000 0.096544 0.095920
0.000000 0.300000 0.276494
0.000000 0.000000 0.002485
0.000000 0.000000 0.000446
0.000000 0.000000 -0.000147
0.000000 0.086550 0.092367
0.000000 0.096544 0.098587
0.000000 0.096544 0.099258
0.000000 0.096544 0.096223
0.000000 0.096544 0.089681
0.000000 0.064085 0.065053
0.000000 0.024148 0.025452
0.000000 0.300000 0.256237
0.000000 0.000000 0.001064
0.000000 0.000000 0.000313
0.000000 0.000000 0.000113
0.000000 0.000000 -0.000010
0.000000 0.000000 -0.000551
0.000000 0.000000 -0.000788
0.000000 0.000000 -0.000805
0.000000 0.000000 -0.000820
0.000000 0.000000 -0.000851
0.000000 0.000000 -0.000917
0.000000 0.000000 -0.000974
0.000000 0.076566 0.074580
0.000000 0.051605 0.049921
0.000000 0.049109 0.047053
0.000000 0.046613 0.043914
0.000000 0.001683 -0.000354
0.000000 0.059093 0.052411
0.000000 0.000000 -0.000198
0.000000 0.000000 -0.000292
0.000000 0.000000 -0.000474
0.000000 0.000000 -0.000643
0.000000 0.000000 -0.000811
0.000000 0.000000 -0.000921
0.000000 0.000000 -0.000985
0.000000 0.000000 -0.001003
0.000000 0.000000 -0.001013
0.000000 0.000000 -0.001037
0.000000 0.000000 -0.001034
0.000000 0.011668 0.001340
0.000000 0.000000 -0.001090
0.000000 0.000000 -0.000992
0.000000 0.046613 0.043743
0.000000 0.049109 0.053167
0.000000 0.000000 -0.000829
0.000000 0.000000 -0.000635
0.000000 0.000000 -0.000608
0.000000 0.000000 -0.000719
0.000000 0.000000 -0.000815
0.000000 0.000000 -0.000854
0.000000 0.148962 0.134175
0.000000 0.000000 -0.000448
0.000000 -0.258819 -0.264008
0.000000 0.000000 -0.002204
0.000000 0.176419 0.156027
0.000000 0.000000 -0.000188
0.000000 0.036628 0.036654
0.000000 0.061589 0.064344
0.000000 0.116513 0.111133
0.000000 0.000000 0.000728
0.000000 0.000000 -0.000514
0.000000 0.000000 -0.000410
0.000000 0.044117 0.038914
0.000000 0.026644 0.025802
0.000000 0.049109 0.045705
0.000000 0.000000 -0.000705
0.000000 0.000000 -0.000560
0.000000 0.069078 0.062285
0.000000 0.000000 -0.000545
0.000000 0.000000 -0.000670
0.000000 0.000000 -0.000656
0.000000 0.000000 -0.000709
0.000000 0.000000 -0.000982
0.000000 0.000000 0.003053
0.000000 -0.024167 -0.013903
0.000000 -0.039144 -0.041902
0.000000 -0.044136 -0.049335
0.000000 -0.004199 -0.002911
0.000000 -0.019175 -0.013472
0.000000 -0.014183 -0.011711
0.000000 -0.039144 -0.021912
0.000000 -0.039144 -0.043741
0.000000 -0.014183 -0.006285
0.000000 -0.069097 -0.068760
0.000000 -0.001702 -0.002442
0.000000 0.001683 -0.001516
0.000000 0.106529 -0.001147
0.000000 -0.046632 -0.046422
0.000000 -0.004199 -0.001742
0.000000 -0.004199 -0.001897
0.000000 -0.004199 -0.001696
0.000000 -0.004199 -0.002067
0.000000 -0.004199 -0.002139
0.000000 -0.029159 -0.041369
0.000000 -0.041640 -0.047406
0.000000 -0.044136 -0.045060
0.000000 0.000000 -0.001983
0.000000 0.000000 -0.001305
0.000000 0.000000 -0.001393
0.000000 0.000000 -0.001094
0.000000 0.000000 -0.000969
0.000000 0.000000 -0.000971
0.000000 0.000000 -0.001043
0.000000 0.000000 -0.001075
0.000000 0.000000 -0.001054
0.000000 0.039125 0.034041
0.000000 0.076566 0.073399
0.000000 0.000000 -0.001029
0.000000 -0.026663 -0.027134
0.000000 0.000000 -0.000781
0.000000 0.000000 -0.000674
0.000000 0.000000 -0.000818
0.000000 0.000000 -0.000964
0.000000 0.000000 -0.000943
0.000000 0.000000 -0.000878
0.000000 0.000000 -0.000867
0.000000 0.000000 -0.000975
0.000000 -0.011687 -0.002192
0.000000 -0.016679 -0.017288
0.000000 -0.026663 -0.024306
0.000000 -0.031656 -0.033497
0.000000 -0.061609 -0.070491
0.000000 -0.009191 -0.002480
0.000000 0.000000 -0.001558
0.000000 -0.021671 -0.019408
0.000000 -0.031656 -0.036113
0.000000 -0.056616 -0.060250
0.000000 -0.021671 -0.025830
0.000000 -0.029159 -0.036613
0.000000 -0.051624 -0.057525
0.000000 -0.029159 -0.034902
0.000000 -0.026663 -0.032235
0.000000 -0.059113 -0.063676
0.000000 -0.086570 -0.076569
0.000000 -0.086570 -0.092692
0.000000 -0.089066 -0.082393
0.000000 -0.094068 -0.094186
0.000000 -0.099060 -0.104615
0.000000 -0.071593 -0.067697
0.000000 -0.056616 -0.057151
0.000000 -0.094068 -0.096163
0.000000 -0.099060 -0.099384
0.000000 -0.071593 -0.076000
0.000000 -0.079081 -0.084837
0.000000 -0.091571 -0.099782
0.000000 -0.091571 -0.098540
0.000000 -0.089066 -0.092713
0.000000 0.000000 -0.003389
0.000000 0.000000 -0.002540
0.000000 0.000000 -0.001264
0.000000 0.000000 -0.001436
0.000000 0.000000 -0.001187
0.000000 -0.064105 -0.062232
0.000000 -0.044136 -0.049030
0.000000 -0.029159 -0.031806
0.000000 0.000000 -0.001851
0.000000 0.000000 -0.001060
0.000000 0.000000 -0.001025
0.000000 0.000000 -0.001051
0.000000 0.000000 -0.001031
0.000000 0.000000 -0.000988
0.000000 0.000000 -0.000978
0.000000 0.000000 -0.001009
0.000000 0.096544 0.096460
0.000000 0.059093 0.055174
0.000000 0.036628 0.035942
0.000000 0.014164 0.010254
0.000000 0.084054 0.077536
0.000000 0.138978 0.128189
0.000000 0.000000 -0.000038
0.000000 0.106529 0.107620
0.000000 0.161443 0.158347
0.000000 0.004179 0.003768
0.000000 0.071574 0.068867
0.000000 0.106529 0.105142
0.000000 0.094048 0.090798
0.000000 0.074070 0.068505
0.000000 0.071574 0.068781
0.000000 0.071574 0.069850
0.000000 0.036628 0.038546
0.000000 0.071574 0.074450
0.000000 0.109025 0.101728
0.000000 0.046613 0.032888
0.000000 0.109025 0.108211
0.000000 0.001683 0.000237
0.000000 0.049109 0.051240
0.000000 0.000000 -0.000353
0.000000 0.069078 0.068070
0.000000 0.109025 0.108281
0.000000 0.074070 0.078782
0.000000 0.000000 -0.000298
0.000000 0.000000 -0.000422
0.000000 0.000000 -0.000497
0.000000 0.000000 -0.000547
0.000000 0.041621 0.044005
0.000000 0.000000 -0.000895
0.000000 0.000000 -0.000727
0.000000 0.000000 -0.000707
0.000000 0.046613 -0.000833
0.000000 0.001683 -0.000960
0.000000 0.000000 -0.000991
0.000000 0.000000 -0.000931
0.000000 -0.000000 -0.003264
0.000000 0.198913 0.191594
0.000000 -0.000000 -0.002161
0.000000 -0.106529 -0.110220
0.000000 -0.124001 -0.123620
0.000000 -0.089046 -0.096330
0.000000 -0.084054 -0.089130
0.000000 -0.044117 -0.045624
0.000000 -0.041621 -0.044060
0.000000 -0.039125 -0.040351
0.000000 -0.000000 -0.002775
0.000000 -0.074070 -0.072885
0.000000 -0.131490 -0.115549
0.000000 -0.000000 -0.003057
0.000000 -0.000000 -0.002247
0.000000 -0.000000 -0.001574
0.000000 -0.044117 -0.044670
0.000000 -0.074070 -0.070633
0.000000 -0.109025 -0.112634
0.000000 -0.056597 -0.058194
0.000000 -0.006675 -0.012713
0.000000 -0.044117 -0.048061
0.000000 -0.300000 -0.290743
0.000000 -0.000000 -0.004393
0.000000 -0.221359 -0.226329
0.000000 0.034152 0.000198
0.000000 -0.000000 -0.003791
0.000000 -0.000000 -0.003107
0.000000 -0.000000 -0.002185
0.000000 -0.000000 -0.001524
0.000000 -0.061589 -0.067888
0.000000 -0.061589 -0.065415
0.000000 -0.061589 -0.064827
0.000000 -0.081558 -0.083015
0.000000 -0.138978 -0.136673
0.000000 -0.000000 -0.002873
0.000000 -0.004179 -0.003179
0.000000 -0.076566 -0.088089
0.000000 -0.104033 -0.104417
0.000000 -0.131490 -0.140388
0.000000 -0.061589 -0.064352
0.000000 -0.061589 -0.067293
0.000000 -0.061589 -0.067260
0.000000 -0.000000 -0.003376
0.000000 -0.000000 -0.002150
0.000000 -0.044117 -0.044582
0.000000 -0.044117 -0.049203
0.000000 -0.079062 -0.091940
0.000000 -0.114017 -0.106223
0.000000 -0.000000 -0.002670
0.000000 -0.076566 -0.077146
0.000000 -0.266288 -0.285767
0.000000 -0.300000 -0.302667
0.000000 -0.300000 -0.300099
0.000000 -0.158947 -0.172899
0.000000 -0.069078 -0.091933
0.000000 -0.300000 -0.266496
0.000000 -0.258800 -0.248583
0.000000 -0.138978 -0.148095
0.000000 -0.036628 -0.043145
0.000000 -0.196388 -0.196117
0.000000 -0.286257 -0.297781
0.000000 -0.091552 -0.091832
0.000000 -0.111521 -0.116130
0.000000 -0.163939 -0.157723
0.000000 -0.101537 -0.107083
0.000000 -0.213870 -0.219619
0.000000 -0.099040 -0.094391
0.000000 -0.009171 -0.010081
0.000000 -0.186404 -0.186689
0.000000 -0.136482 -0.141569
0.000000 -0.000000 -0.005047
0.000000 -0.000000 -0.003282
0.000000 -0.000000 -0.002628
0.000000 -0.000000 -0.002132
0.000000 -0.031636 -0.035134
0.000000 -0.101537 -0.097987
0.000000 -0.089046 -0.095811
0.000000 -0.071574 -0.071108
0.000000 -0.054101 -0.060809
0.000000 -0.054101 -0.058305
0.000000 -0.116513 -0.116311
0.000000 -0.064085 -0.065106
0.000000 -0.133986 -0.134886
0.000000 -0.089046 -0.090917
0.000000 -0.059093 -0.065667
0.000000 -0.151458 -0.146940
0.000000 -0.121505 -0.125588
0.000000 -0.101537 -0.099871
0.000000 -0.101537 -0.108154
0.000000 -0.101537 -0.107148
0.000000 -0.089046 -0.093228
0.000000 -0.086550 -0.089858
0.000000 -0.086550 -0.093674
0.000000 -0.086550 -0.084683
0.000000 -0.051605 -0.053720
0.000000 -0.000000 -0.003797
0.000000 -0.000000 -0.002337
0.000000 -0.000000 -0.001891
0.000000 -0.009171 -0.012462
0.000000 -0.064085 -0.063304
0.000000 -0.064085 -0.066437
0.000000 -0.074070 -0.077676
0.000000 -0.056597 -0.059969
0.000000 -0.056597 -0.062245
0.000000 -0.054101 -0.055592
0.000000 -0.079062 -0.077324
0.000000 -0.000000 -0.002323
0.000000 -0.106529 -0.106744
0.000000 -0.136482 -0.139466
0.000000 -0.099040 -0.105033
0.000000 -0.076566 -0.076048
0.000000 -0.049109 -0.054265
0.000000 -0.300000 -0.287263
0.000000 -0.208878 -0.227109
0.000000 -0.218863 -0.233789
0.000000 -0.000000 -0.006120
0.000000 -0.000000 -0.004589
0.000000 0.178935 0.163869
0.000000 -0.034132 -0.037983
0.000000 -0.056597 -0.057158
0.000000 -0.000000 -0.001591
0.000000 -0.000000 -0.001373
0.000000 -0.000000 -0.001285
0.000000 -0.133986 -0.153692
0.000000 -0.131490 -0.136160
0.000000 -0.000000 -0.003610
0.000000 -0.054101 -0.058529
0.000000 -0.059093 -0.064346
0.000000 -0.000000 -0.002959
0.000000 -0.056597 -0.056216
0.000000 -0.056597 -0.059432
0.000000 -0.056597 -0.061128
0.000000 -0.069078 -0.067378
0.000000 -0.069078 -0.073797
0.000000 -0.041621 -0.049162
0.000000 -0.041621 -0.045209
0.000000 -0.041621 -0.048329
0.000000 -0.041621 -0.046499
0.000000 -0.041621 -0.047470
0.000000 -0.126497 -0.127925
0.000000 -0.133986 -0.137258
0.000000 -0.133986 -0.150776
0.000000 -0.016660 -0.012458
0.000000 -0.064085 -0.066441
0.000000 -0.000000 -0.003568
0.000000 -0.000000 -0.002478
0.000000 -0.000000 -0.001662
0.000000 -0.019156 -0.024323
0.000000 -0.049109 -0.052323
0.000000 -0.061589 -0.064355
0.000000 -0.064085 -0.065566
0.000000 -0.066582 -0.067036
0.000000 -0.066582 -0.068331
0.000000 -0.069078 -0.073400
0.000000 -0.019156 -0.026424
0.000000 -0.128994 -0.126535
0.000000 -0.000000 -0.003210
0.000000 -0.096544 -0.094054
0.000000 -0.099040 -0.106311
0.000000 -0.094048 -0.102457
0.000000 -0.000000 -0.003267
0.000000 -0.000000 -0.002759
0.000000 -0.014164 -0.019875
0.000000 -0.101537 -0.100909
0.000000 -0.133986 -0.130054
0.000000 -0.218863 -0.218357
0.000000 -0.000000 -0.004945
0.000000 -0.000000 -0.002169
0.000000 -0.049109 -0.057119
0.000000 -0.276273 -0.209541
0.000000 -0.288753 -0.278721
0.000000 -0.246320 -0.255660
0.000000 -0.131490 -0.142259
0.000000 -0.201390 -0.205048
0.000000 -0.121505 -0.118128
0.000000 -0.300000 -0.296879
0.000000 -0.233839 -0.246071
0.000000 -0.011668 -0.016563
0.000000 -0.213870 -0.218796
0.000000 -0.298738 -0.298325
0.000000 -0.024148 -0.035748
0.000000 -0.046613 -0.045813
0.000000 -0.151458 -0.151212
0.000000 -0.178915 -0.172093
0.000000 -0.173923 -0.172584
0.000000 -0.173923 -0.169222
0.000000 -0.109025 -0.118903
0.000000 -0.000000 -0.006223
0.000000 -0.091552 -0.099596
0.000000 -0.094048 -0.096011
0.000000 -0.000000 -0.004600
0.000000 -0.056597 -0.058408
0.000000 -0.000000 -0.004745
0.000000 -0.000000 -0.002748
0.000000 -0.014164 -0.010951
0.000000 -0.049109 -0.051862
0.000000 -0.099040 -0.098099
0.000000 -0.099040 -0.102705
0.000000 -0.024148 -0.026550
0.000000 -0.059093 -0.064352
0.000000 -0.104033 -0.108421
0.000000 -0.109025 -0.105405
0.000000 -0.109025 -0.108374
0.000000 -0.111521 -0.099239
0.000000 -0.133986 -0.132486
0.000000 -0.133986 -0.135131
0.000000 -0.084054 -0.092696
0.000000 -0.126497 -0.120641
0.000000 -0.131490 -0.131729
0.000000 -0.039125 -0.047665
0.000000 -0.086550 -0.095029
0.000000 -0.066582 -0.070450
0.000000 -0.066582 -0.066927
0.000000 -0.089046 -0.089149
0.000000 -0.000000 -0.002936
0.000000 0.178935 0.163749
0.000000 -0.049109 -0.051079
0.000000 -0.079062 -0.088754
0.000000 -0.101537 -0.098996
0.000000 -0.000000 -0.002796
0.000000 -0.168931 -0.172927
0.000000 -0.181411 -0.186160
0.000000 -0.000000 -0.004530
0.000000 -0.000000 -0.002975
0.000000 -0.049109 -0.052450
0.000000 -0.066582 -0.067689
0.000000 -0.111521 -0.115823
0.000000 -0.081558 -0.078492
0.000000 -0.054101 -0.055683
0.000000 -0.000000 -0.003022
0.000000 -0.084054 -0.084741
0.000000 -0.300000 -0.301301
0.000000 -0.300000 -0.266356
0.000000 -0.211374 -0.203306
0.000000 -0.000000 -0.006280
0.000000 -0.000000 -0.005707
0.000000 -0.000000 -0.004918
0.000000 0.300000 0.264274
0.000000 -0.000000 -0.001103
0.000000 -0.009171 -0.004741
0.000000 -0.101537 -0.099427
0.000000 -0.099040 -0.101382
0.000000 -0.094048 -0.100322
0.000000 -0.079062 -0.086576
0.000000 -0.051605 -0.050725
0.000000 -0.000000 -0.002440
0.000000 -0.041621 -0.049714
0.000000 -0.121505 -0.111991
0.000000 -0.000000 -0.003205
0.000000 -0.071574 -0.076099
0.000000 -0.076566 -0.074037
0.000000 -0.016660 -0.021439
0.000000 -0.000000 -0.002932
0.000000 -0.011668 -0.007748
0.000000 -0.081558 -0.078876
0.000000 -0.148962 -0.160038
0.000000 -0.136482 -0.127141
0.000000 -0.000000 -0.003713
0.000000 -0.000000 -0.002916
0.000000 -0.039125 -0.039544
0.000000 -0.138978 -0.141567
0.000000 -0.203886 -0.205372
0.000000 -0.019156 -0.022253
0.000000 -0.046613 -0.052348
0.000000 -0.014164 -0.004082
0.000000 -0.076566 -0.079453
0.000000 -0.021652 -0.024427
0.000000 -0.016660 -0.018733
0.000000 -0.039125 -0.040568
0.000000 -0.044117 -0.047962
0.000000 -0.046613 -0.049038
0.000000 -0.046613 -0.049254
0.000000 -0.046613 -0.051887
0.000000 -0.046613 -0.050613
0.000000 -0.046613 -0.051511
0.000000 -0.046613 -0.052382
0.000000 -0.111521 -0.114988
0.000000 -0.114017 -0.110634
0.000000 -0.044117 -0.049634
0.000000 -0.021652 -0.022614
0.000000 -0.016660 -0.016790
0.000000 -0.094048 -0.093744
0.000000 -0.066582 -0.066896
0.000000 -0.091552 -0.086383
0.000000 -0.121505 -0.125518
0.000000 -0.000000 -0.006200
0.000000 -0.079062 -0.083574
0.000000 -0.218863 -0.207592
0.000000 -0.266288 -0.264491
0.000000 -0.218863 -0.215528
0.000000 -0.151458 -0.160528
0.000000 -0.151458 -0.156026
0.000000 -0.151458 -0.151734
0.000000 -0.151458 -0.157910
0.000000 -0.268784 -0.262211
0.000000 -0.258800 -0.249954
0.000000 -0.094048 -0.104261
0.000000 -0.096544 -0.093479
0.000000 -0.143970 -0.149228
0.000000 -0.156451 -0.152292
0.000000 -0.131490 -0.133648
0.000000 -0.011668 -0.011882
0.000000 -0.183908 -0.181461
0.000000 -0.256304 -0.264914
0.000000 -0.059093 -0.068379
0.000000 -0.119009 -0.120190
0.000000 -0.041621 -0.043835
0.000000 0.116532 0.100438
0.000000 -0.041621 -0.042092
0.000000 -0.076566 -0.075915
0.000000 -0.119009 -0.111456
0.000000 -0.131490 -0.122066
0.000000 -0.054101 -0.056971
0.000000 -0.000000 -0.003432
0.000000 -0.000000 -0.002165
0.000000 -0.021652 -0.024174
0.000000 -0.056597 -0.053179
0.000000 -0.064085 -0.065434
0.000000 -0.086550 -0.093212
0.000000 -0.101537 -0.099830
0.000000 -0.131490 -0.133816
0.000000 -0.158947 -0.156614
0.000000 -0.158947 -0.160059
0.000000 -0.056597 -0.059271
0.000000 -0.114017 -0.112177
0.000000 -0.114017 -0.117316
0.000000 -0.114017 -0.108484
0.000000 -0.114017 -0.114938
0.000000 -0.114017 -0.116545
0.000000 -0.114017 -0.116358
0.000000 -0.081558 -0.086614
0.000000 -0.064085 -0.072909
0.000000 -0.000000 -0.004403
0.000000 -0.000000 -0.002871
0.000000 -0.000000 -0.001999
0.000000 -0.000000 -0.001534
0.000000 -0.000000 -0.001390
0.000000 -0.000000 -0.001220
0.000000 -0.074070 -0.076649
0.000000 -0.124001 -0.131782
0.000000 -0.163939 -0.162211
0.000000 -0.000000 -0.003541
0.000000 -0.000000 -0.002204
0.000000 -0.049109 -0.057242
0.000000 -0.059093 -0.065358
0.000000 -0.206382 -0.206660
0.000000 -0.156451 -0.156311
0.000000 -0.000000 -0.003521
0.000000 -0.000000 -0.003377
0.000000 -0.121505 -0.113274
0.000000 -0.300000 -0.305936
0.000000 -0.300000 -0.292256
0.000000 -0.268784 -0.260186
0.000000 0.300000 0.273196
0.000000 -0.000000 -0.000589
0.000000 -0.000000 -0.001939
0.000000 -0.000000 -0.001718
0.000000 -0.046613 -0.044396
0.000000 0.084074 0.081285
0.000000 -0.000000 -0.001895
0.000000 -0.000000 -0.001339
0.000000 -0.089046 -0.087917
0.000000 -0.153954 -0.160772
0.000000 -0.104033 -0.108447
0.000000 -0.000000 -0.003066
0.000000 -0.000000 -0.002934
0.000000 -0.148962 -0.132145
0.000000 -0.054101 -0.057629
0.000000 -0.000000 -0.003663
0.000000 0.000000 -0.002264
0.000000 -0.198913 -0.193632
0.000000 0.000000 -0.002910
0.000000 0.106529 0.100535
0.000000 0.124001 0.118221
0.000000 0.089046 0.092553
0.000000 0.084054 0.078898
0.000000 0.044117 0.044232
0.000000 0.041621 0.040583
0.000000 0.039125 0.042027
0.000000 0.000000 0.000270
0.000000 0.074070 0.077262
0.000000 0.131490 0.121158
0.000000 0.000000 0.000949
0.000000 0.000000 0.000293
0.000000 0.000000 0.000021
0.000000 0.044117 0.047447
0.000000 0.074070 0.076020
0.000000 0.109025 0.108751
0.000000 0.056597 0.055121
0.000000 0.006675 0.003346
0.000000 0.044117 0.044337
0.000000 0.300000 0.265360
0.000000 0.000000 0.002262
0.000000 0.221359 0.141170
0.000000 -0.034152 -0.035486
0.000000 0.000000 -0.000250
0.000000 0.000000 -0.000586
0.000000 0.000000 -0.000743
0.000000 0.000000 -0.000882
0.000000 0.061589 0.057046
0.000000 0.061589 0.058320
0.000000 0.061589 0.058862
0.000000 0.081558 0.076690
0.000000 0.138978 0.126683
0.000000 0.000000 -0.000127
0.000000 0.004179 0.006757
0.000000 0.076566 0.075712
0.000000 0.104033 0.106150
0.000000 0.131490 0.124665
0.000000 0.061589 0.064177
0.000000 0.061589 0.060991
0.000000 0.061589 0.061202
0.000000 0.000000 0.000571
0.000000 0.000000 0.000091
0.000000 0.044117 0.047727
0.000000 0.044117 0.046691
0.000000 0.079062 0.083651
0.000000 0.114017 0.111260
0.000000 0.000000 0.000003
0.000000 0.076566 0.078584
0.000000 0.266288 0.253005
0.000000 0.300000 0.300095
0.000000 0.300000 0.286962
0.000000 0.158947 0.147040
0.000000 0.069078 0.071832
0.000000 0.300000 0.249850
0.000000 0.258800 0.250639
0.000000 0.138978 0.156292
0.000000 0.036628 0.044762
0.000000 0.196388 0.174634
0.000000 0.286257 0.281708
0.000000 0.091552 0.100462
0.000000 0.111521 0.114529
0.000000 0.163939 0.160138
0.000000 0.101537 0.099793
0.000000 0.213870 0.207572
0.000000 0.099040 0.100953
0.000000 0.009171 0.009836
0.000000 0.186404 0.164696
0.000000 0.136482 0.137895
0.000000 0.000000 0.000877
0.000000 0.000000 0.000563
0.000000 0.000000 0.000276
0.000000 0.000000 -0.000207
0.000000 0.031636 0.023980
0.000000 0.101537 0.103077
0.000000 0.089046 0.092184
0.000000 0.071574 0.066782
0.000000 0.054101 0.048541
0.000000 0.054101 0.052658
0.000000 0.116513 0.108557
0.000000 0.064085 0.067888
0.000000 0.133986 0.132496
0.000000 0.089046 0.091570
0.000000 0.059093 0.059624
0.000000 0.151458 0.169919
0.000000 0.121505 0.110003
0.000000 0.101537 0.102289
0.000000 0.101537 0.105484
0.000000 0.101537 0.100459
0.000000 0.089046 0.096442
0.000000 0.086550 0.087983
0.000000 0.086550 0.091806
0.000000 0.086550 0.078520
0.000000 0.051605 0.052839
0.000000 0.000000 0.000351
0.000000 0.000000 -0.000060
0.000000 0.000000 -0.000369
0.000000 0.009171 -0.000282
0.000000 0.064085 0.061278
0.000000 0.064085 0.061407
0.000000 0.074070 0.074023
0.000000 0.056597 0.053217
0.000000 0.056597 0.061243
0.000000 0.054101 0.054105
0.000000 0.079062 0.077895
0.000000 0.000000 0.000387
0.000000 0.106529 0.103914
0.000000 0.136482 0.143282
0.000000 0.099040 0.094190
0.000000 0.076566 0.078268
0.000000 0.049109 0.048224
0.000000 0.300000 0.278158
0.000000 0.208878 0.200532
0.000000 0.218863 0.210273
0.000000 0.000000 0.004221
0.000000 0.000000 0.000998
0.000000 -0.178935 -0.161722
0.000000 0.034132 0.039643
0.000000 0.056597 0.054805
0.000000 0.000000 -0.000973
0.000000 0.000000 -0.000725
0.000000 0.000000 -0.000765
0.000000 0.133986 0.127147
0.000000 0.131490 0.132855
0.000000 0.000000 -0.000460
0.000000 0.054101 0.049390
0.000000 0.059093 0.061110
0.000000 0.000000 -0.000138
0.000000 0.056597 0.069793
0.000000 0.056597 0.058587
0.000000 0.056597 0.052944
0.000000 0.069078 0.070125
0.000000 0.069078 0.071082
0.000000 0.041621 0.041439
0.000000 0.041621 0.043556
0.000000 0.041621 0.040651
0.000000 0.041621 0.043514
0.000000 0.041621 0.042780
0.000000 0.126497 0.124421
0.000000 0.133986 0.132113
0.000000 0.133986 0.123277
0.000000 0.016660 0.014426
0.000000 0.064085 0.061289
0.000000 0.000000 0.000400
0.000000 0.000000 0.000099
0.000000 0.000000 -0.000333
0.000000 0.019156 0.006005
0.000000 0.049109 0.050703
0.000000 0.061589 0.059157
0.000000 0.064085 0.061571
0.000000 0.066582 0.066227
0.000000 0.066582 0.063195
0.000000 0.069078 0.061473
0.000000 0.019156 0.023534
0.000000 0.128994 0.130996
0.000000 0.000000 0.000534
0.000000 0.096544 0.090048
0.000000 0.099040 0.104891
0.000000 0.094048 0.094627
0.000000 0.000000 -0.000071
0.000000 0.000000 -0.000121
0.000000 0.014164 0.006359
0.000000 0.101537 0.095603
0.000000 0.133986 0.131191
0.000000 0.218863 0.202433
0.000000 0.000000 0.000503
0.000000 0.000000 0.004340
0.000000 0.049109 0.052624
0.000000 0.276273 0.203362
0.000000 0.288753 0.319283
0.000000 0.246320 0.212487
0.000000 0.131490 0.115534
0.000000 0.201390 0.192231
0.000000 0.121505 0.115552
0.000000 0.300000 0.266273
0.000000 0.233839 0.196809
0.000000 0.011668 0.006703
0.000000 0.213870 0.215814
0.000000 0.298738 0.237943
0.000000 0.024148 0.026641
0.000000 0.046613 0.051311
0.000000 0.151458 0.153719
0.000000 0.178915 0.170903
0.000000 0.173923 0.177773
0.000000 0.173923 0.175033
0.000000 0.109025 0.112041
0.000000 0.000000 0.001005
0.000000 0.091552 0.104169
0.000000 0.094048 0.111637
0.000000 0.000000 0.000975
0.000000 0.056597 0.059448
0.000000 0.000000 -0.000760
0.000000 0.000000 -0.000400
0.000000 0.014164 0.002899
0.000000 0.049109 0.045917
0.000000 0.099040 0.095448
0.000000 0.099040 0.097761
0.000000 0.024148 0.013367
0.000000 0.059093 0.058204
0.000000 0.104033 0.104789
0.000000 0.109025 0.106822
0.000000 0.109025 0.100059
0.000000 0.111521 0.105975
0.000000 0.133986 0.117513
0.000000 0.133986 0.123373
0.000000 0.084054 0.092563
0.000000 0.126497 0.117669
0.000000 0.131490 0.126309
0.000000 0.039125 0.038842
0.000000 0.086550 0.087377
0.000000 0.066582 0.067449
0.000000 0.066582 0.068219
0.000000 0.089046 0.082055
0.000000 0.000000 0.000816
0.000000 -0.178935 -0.181573
0.000000 0.049109 0.048602
0.000000 0.079062 0.070765
0.000000 0.101537 0.098706
0.000000 0.000000 -0.000885
0.000000 0.168931 0.167903
0.000000 0.181411 0.171587
0.000000 0.000000 0.001530
0.000000 0.000000 0.000400
0.000000 0.049109 0.050862
0.000000 0.066582 0.067115
0.000000 0.111521 0.122457
0.000000 0.081558 0.085190
0.000000 0.054101 0.052480
0.000000 0.000000 0.004613
0.000000 0.084054 0.084923
0.000000 0.300000 0.288150
0.000000 0.300000 0.289465
0.000000 0.211374 0.218366
0.000000 0.000000 0.001772
0.000000 0.000000 0.000922
0.000000 0.000000 0.000967
0.000000 -0.300000 -0.280619
0.000000 0.000000 -0.001225
0.000000 0.009171 0.003127
0.000000 0.101537 0.098198
0.000000 0.099040 0.096768
0.000000 0.094048 0.092971
0.000000 0.079062 0.072339
0.000000 0.051605 0.049462
0.000000 0.000000 -0.000570
0.000000 0.041621 0.043765
0.000000 0.121505 0.108269
0.000000 0.000000 0.000718
0.000000 0.071574 0.074669
0.000000 0.076566 0.079411
0.000000 0.016660 0.012570
0.000000 0.000000 0.000413
0.000000 0.011668 0.001752
0.000000 0.081558 0.074863
0.000000 0.148962 0.129719
0.000000 0.136482 0.138915
0.000000 0.000000 0.000125
0.000000 0.000000 0.000328
0.000000 0.039125 0.033526
0.000000 0.138978 0.127305
0.000000 0.203886 0.181033
0.000000 0.019156 0.021872
0.000000 0.046613 0.045938
0.000000 0.014164 0.014766
0.000000 0.076566 0.071975
0.000000 0.021652 0.021020
0.000000 0.016660 0.017505
0.000000 0.039125 0.040500
0.000000 0.044117 0.037389
0.000000 0.046613 0.042194
0.000000 0.046613 0.043910
0.000000 0.046613 0.043009
0.000000 0.046613 0.042813
0.000000 0.046613 0.050506
0.000000 0.046613 0.045734
0.000000 0.111521 0.108484
0.000000 0.114017 0.112224
0.000000 0.044117 0.043642
0.000000 0.021652 0.016744
0.000000 0.016660 0.017434
0.000000 0.094048 0.088181
0.000000 0.066582 0.066386
0.000000 0.091552 0.089768
0.000000 0.121505 0.111668
0.000000 0.000000 0.006490
0.000000 0.079062 0.094120
0.000000 0.218863 0.157038
0.000000 0.266288 0.256272
0.000000 0.218863 0.212746
0.000000 0.151458 0.151240
0.000000 0.151458 0.145537
0.000000 0.151458 0.150734
0.000000 0.151458 0.123677
0.000000 0.268784 0.237229
0.000000 0.258800 0.228667
0.000000 0.094048 0.096077
0.000000 0.096544 0.092197
0.000000 0.143970 0.149072
0.000000 0.156451 0.160855
0.000000 0.131490 0.129850
0.000000 0.011668 0.006396
0.000000 0.183908 0.180333
0.000000 0.256304 0.242242
0.000000 0.059093 0.068539
0.000000 0.119009 0.123005
0.000000 0.041621 0.041553
0.000000 -0.116532 -0.121414
0.000000 0.041621 0.041152
0.000000 0.076566 0.084520
0.000000 0.119009 0.111302
0.000000 0.131490 0.124971
0.000000 0.054101 0.051346
0.000000 0.000000 0.000055
0.000000 0.000000 -0.000107
0.000000 0.021652 0.016663
0.000000 0.056597 0.059296
0.000000 0.064085 0.068848
0.000000 0.086550 0.093655
0.000000 0.101537 0.096815
0.000000 0.131490 0.126745
0.000000 0.158947 0.154455
0.000000 0.158947 0.157132
0.000000 0.056597 0.056364
0.000000 0.114017 0.114350
0.000000 0.114017 0.112726
0.000000 0.114017 0.111345
0.000000 0.114017 0.104528
0.000000 0.114017 0.111591
0.000000 0.114017 0.110559
0.000000 0.081558 0.078485
0.000000 0.064085 0.063452
0.000000 0.000000 0.000728
0.000000 0.000000 0.000235
0.000000 0.000000 -0.000202
0.000000 0.000000 -0.000424
0.000000 0.000000 -0.000556
0.000000 0.000000 -0.000669
0.000000 0.074070 0.065950
0.000000 0.124001 0.123965
0.000000 0.163939 0.174008
0.000000 0.000000 0.000637
0.000000 0.000000 0.000116
0.000000 0.049109 0.046857
0.000000 0.059093 0.058893
0.000000 0.206382 0.203215
0.000000 0.156451 0.149862
0.000000 0.000000 0.000342
0.000000 0.000000 0.000107
0.000000 0.121505 0.109923
0.000000 0.300000 0.281857
0.000000 0.300000 0.298539
0.000000 0.268784 0.246460
0.000000 -0.300000 -0.304607
0.000000 0.000000 -0.000989
0.000000 0.000000 -0.000552
0.000000 0.000000 -0.000702
0.000000 0.046613 0.046375
0.000000 -0.084074 -0.093651
0.000000 0.000000 -0.001092
0.000000 0.000000 -0.000853
0.000000 0.089046 0.091060
0.000000 0.153954 0.142706
0.000000 0.104033 0.094599
0.000000 0.000000 -0.000603
0.000000 0.000000 0.000235
0.000000 0.148962 0.142012
0.000000 0.054101 0.054444
In [14]:
conv_predictions = X_valid[:,-1]
recurrent_predictions = model.predict(X_valid)
ground_truth = Y_valid
for x,y,z in zip(conv_predictions[:,0], ground_truth, recurrent_predictions[:,0]):
print ("{:07f}\t{:07f}\t{:07f}\t".format(x,y,z))
0.000000 0.193911 0.081758
0.000000 0.173942 0.076677
0.000000 0.119028 0.014650
0.000000 0.084074 -0.000172
0.000000 0.014183 -0.004128
0.000000 -0.000000 -0.088243
0.000000 0.029159 -0.082580
0.000000 0.001702 -0.083135
0.000000 -0.000000 -0.002743
0.000000 -0.000000 -0.002158
0.000000 -0.000000 -0.080231
0.000000 -0.000000 0.173954
0.000000 -0.000000 0.167893
0.000000 -0.000000 -0.098979
0.000000 0.046632 -0.107860
0.000000 0.300000 -0.001017
0.000000 0.300000 -0.036726
0.000000 0.300000 -0.039343
0.000000 0.300000 -0.060210
0.000000 0.300000 -0.001295
0.000000 0.300000 0.066367
0.000000 -0.000000 -0.001154
0.000000 -0.000000 -0.002765
0.000000 -0.099040 -0.063886
0.000000 -0.044117 -0.055263
0.000000 -0.000000 -0.001552
0.000000 -0.000000 -0.001260
0.000000 -0.000000 -0.001398
0.000000 -0.000000 0.003892
0.000000 -0.000000 0.081466
0.000000 -0.000000 0.000975
0.000000 0.104052 0.007476
0.000000 0.096564 -0.082542
0.000000 0.034152 -0.004605
0.000000 0.106548 -0.036792
0.000000 0.178935 -0.019905
0.000000 0.166454 -0.001212
0.000000 0.129013 -0.001005
0.000000 0.129013 -0.001017
0.000000 0.126517 -0.000761
0.000000 0.126517 -0.000406
0.000000 0.039144 0.043902
0.000000 0.116532 0.084014
0.000000 0.136501 0.079460
0.000000 0.021671 0.037174
0.000000 -0.000000 -0.000037
0.000000 0.049128 -0.000468
0.000000 -0.000000 -0.000532
0.000000 -0.000000 0.004080
0.000000 -0.000000 0.077464
0.000000 -0.000000 -0.016794
0.000000 0.064105 -0.009701
0.000000 0.300000 -0.039424
0.000000 0.300000 -0.064249
0.000000 0.300000 -0.009397
0.000000 0.300000 0.117577
0.000000 0.064105 0.059870
0.000000 0.041640 -0.000762
0.000000 0.081577 -0.000706
0.000000 0.148982 0.039586
0.000000 -0.031636 -0.124632
0.000000 -0.046613 -0.121112
0.000000 -0.086550 -0.130548
0.000000 -0.000000 -0.128613
0.000000 -0.000000 -0.091758
0.000000 -0.000000 0.019543
0.000000 -0.000000 -0.131833
0.000000 -0.000000 -0.009065
0.000000 -0.000000 -0.008933
0.000000 0.009191 0.060200
0.000000 0.163958 0.061502
0.000000 0.213890 0.040738
0.000000 0.213890 0.180248
0.000000 0.213890 -0.016544
0.000000 0.216386 0.088849
0.000000 0.263811 0.091260
0.000000 0.138997 -0.001215
0.000000 -0.000000 -0.000188
0.000000 -0.000000 -0.049172
0.000000 -0.000000 -0.003024
0.000000 0.000000 -0.000894
0.000000 0.000000 0.019083
0.000000 0.000000 0.020166
0.000000 0.000000 0.013614
0.000000 0.000000 -0.000610
0.000000 0.021652 0.055931
0.000000 0.031636 0.021010
0.000000 0.000000 -0.000596
0.000000 0.000000 0.004136
0.000000 0.000000 -0.119760
0.000000 -0.049128 0.032473
0.000000 -0.151478 0.274862
0.000000 -0.178935 0.118322
0.000000 -0.233858 -0.131727
0.000000 -0.261315 -0.049573
0.000000 -0.193911 -0.070714
0.000000 -0.173942 -0.100913
0.000000 -0.119028 -0.129752
0.000000 -0.084074 -0.003298
0.000000 -0.014183 -0.002384
0.000000 0.000000 0.093493
0.000000 -0.029159 0.001017
0.000000 -0.001702 0.001084
0.000000 0.000000 0.071739
0.000000 0.000000 0.073913
0.000000 0.000000 0.001126
0.000000 0.000000 -0.008329
0.000000 0.000000 0.024079
0.000000 0.000000 0.014059
0.000000 -0.046632 0.096195
0.000000 -0.300000 0.007475
0.000000 -0.300000 0.051323
0.000000 -0.300000 0.051255
0.000000 -0.300000 -0.011327
0.000000 -0.300000 0.001796
0.000000 -0.300000 -0.083289
0.000000 0.000000 -0.000991
0.000000 0.000000 -0.000693
0.000000 0.099040 -0.000122
0.000000 0.044117 -0.000881
0.000000 0.000000 -0.000922
0.000000 0.000000 -0.000213
0.000000 0.000000 -0.001020
0.000000 0.000000 -0.001032
0.000000 0.000000 -0.044364
0.000000 0.000000 -0.082304
0.000000 -0.104052 -0.018657
0.000000 -0.096564 -0.001497
0.000000 -0.034152 -0.001223
0.000000 -0.106548 0.000475
0.000000 -0.178935 -0.001030
0.000000 -0.166454 -0.001029
0.000000 -0.129013 -0.001005
0.000000 -0.129013 -0.001050
0.000000 -0.126517 -0.002913
0.000000 -0.126517 -0.077884
0.000000 -0.039144 -0.046764
0.000000 -0.116532 -0.001662
0.000000 -0.136501 -0.090013
0.000000 -0.021671 -0.020758
0.000000 0.000000 -0.002006
0.000000 -0.049128 -0.001395
0.000000 0.000000 -0.001755
0.000000 0.000000 -0.021354
0.000000 0.000000 -0.117937
0.000000 0.000000 -0.002591
0.000000 -0.064105 -0.056122
0.000000 -0.300000 -0.030594
0.000000 -0.300000 -0.002041
0.000000 -0.300000 -0.000944
0.000000 -0.300000 -0.086194
0.000000 -0.064105 -0.114011
0.000000 -0.041640 -0.012291
0.000000 -0.081577 -0.002990
0.000000 -0.148982 0.036817
0.000000 0.031636 0.016725
0.000000 0.046613 0.093150
0.000000 0.086550 0.092362
0.000000 0.000000 0.093414
0.000000 0.000000 0.007207
0.000000 0.000000 -0.000240
0.000000 0.000000 0.006954
0.000000 0.000000 0.057264
0.000000 0.000000 0.011452
0.000000 -0.009191 -0.136083
0.000000 -0.163958 0.017599
0.000000 -0.213890 0.127445
0.000000 -0.213890 -0.163605
0.000000 -0.213890 0.002909
0.000000 -0.216386 -0.047614
0.000000 -0.263811 -0.049916
0.000000 -0.138997 -0.139770
0.000000 0.000000 -0.001853
0.000000 0.000000 0.082127
In [ ]:
plt.plot(ground_truth[:100])
In [ ]:
sns.tsplot(history.history['val_loss'])
In [ ]:
for x,y in zip(X_valid, Y_valid):
print ("{}\t{}".format(np.reshape(x,(10,)),y))
In [ ]:
Content source: no-fire/line-follower
Similar notebooks: