プログラミング

LSTM時系列解析　全体像

2017年12月31日 by 河副太智 Leave a Comment

LSTM時系列解析

import numpy
import matplotlib.pyplot as plt
from pandas import read_csv
import math
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error

#以下にコードを書いてください
# データセットの作成
def create_dataset(dataset, look_back):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return numpy.array(dataX), numpy.array(dataY)
# 乱数設定
numpy.random.seed(7)
# データセットの読み込み　
dataframe = read_csv('nikkei225.csv', usecols=[1], engine='python', skipfooter=3)
dataset = dataframe.values
dataset = dataset.astype('float32')
# 訓練データ、テストデータに分ける
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# データのスケーリング
scaler = MinMaxScaler(feature_range=(0, 1))
scaler_train = scaler.fit(train)
train = scaler_train.transform(train)
test = scaler_train.transform(test)

# データの作成
look_back = 10
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
# データの整形
trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))
testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))
# LSTMモデルの作成と学習
model = Sequential()
model.add(LSTM(64, return_sequences=True,input_shape=(look_back, 1)))
model.add(LSTM(32))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2)

# 予測データの作成
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# スケールしたデータを元に戻す
trainPredict = scaler_train.inverse_transform(trainPredict)
trainY = scaler_train.inverse_transform([trainY])
testPredict = scaler_train.inverse_transform(testPredict)
testY = scaler_train.inverse_transform([testY])

# 予測精度の計算
trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
print('Train Score: %.2f RMSE' % (trainScore))
testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
print('Test Score: %.2f RMSE' % (testScore))

# プロットのためのデータ整形
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# テストデータのプロット
plt.plot(dataframe[round(len(dataset)*0.67):])
plt.plot(testPredictPlot)
plt.show()

import numpy

import matplotlib.pyplot as plt

from pandas import read_csv

import math

from keras.models import Sequential

from keras.layers import Dense

from keras.layers import LSTM

from sklearn.preprocessing import MinMaxScaler

from sklearn.metrics import mean_squared_error

#以下にコードを書いてください

# データセットの作成

def create_dataset(dataset, look_back):

dataX, dataY = [], []

for i in range(len(dataset)-look_back-1):

a = dataset[i:(i+look_back), 0]

dataX.append(a)

dataY.append(dataset[i + look_back, 0])

return numpy.array(dataX), numpy.array(dataY)

# 乱数設定

numpy.random.seed(7)

# データセットの読み込み　

dataframe = read_csv('nikkei225.csv', usecols=[1], engine='python', skipfooter=3)

dataset = dataframe.values

dataset = dataset.astype('float32')

# 訓練データ、テストデータに分ける

train_size = int(len(dataset) * 0.67)

test_size = len(dataset) - train_size

train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]

# データのスケーリング

scaler = MinMaxScaler(feature_range=(0, 1))

scaler_train = scaler.fit(train)

train = scaler_train.transform(train)

test = scaler_train.transform(test)

# データの作成

look_back = 10

trainX, trainY = create_dataset(train, look_back)

testX, testY = create_dataset(test, look_back)

# データの整形

trainX = numpy.reshape(trainX, (trainX.shape[0], trainX.shape[1], 1))

testX = numpy.reshape(testX, (testX.shape[0], testX.shape[1], 1))

# LSTMモデルの作成と学習

model = Sequential()

model.add(LSTM(64, return_sequences=True,input_shape=(look_back, 1)))

model.add(LSTM(32))

model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')

model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2)

# 予測データの作成

trainPredict = model.predict(trainX)

testPredict = model.predict(testX)

# スケールしたデータを元に戻す

trainPredict = scaler_train.inverse_transform(trainPredict)

trainY = scaler_train.inverse_transform([trainY])

testPredict = scaler_train.inverse_transform(testPredict)

testY = scaler_train.inverse_transform([testY])

# 予測精度の計算

trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))

print('Train Score: %.2f RMSE' % (trainScore))

testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))

print('Test Score: %.2f RMSE' % (testScore))

# プロットのためのデータ整形

trainPredictPlot = numpy.empty_like(dataset)

trainPredictPlot[:, :] = numpy.nan

trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict

testPredictPlot = numpy.empty_like(dataset)

testPredictPlot[:, :] = numpy.nan

testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict

# テストデータのプロット

plt.plot(dataframe[round(len(dataset)*0.67):])

plt.plot(testPredictPlot)

plt.show()

結果

Epoch 1/10
44s - loss: 0.0040
Epoch 2/10
44s - loss: 0.0013
Epoch 3/10
43s - loss: 0.0011
Epoch 4/10
44s - loss: 7.8079e-04
Epoch 5/10
44s - loss: 5.9064e-04
Epoch 6/10
44s - loss: 5.5586e-04
Epoch 7/10
43s - loss: 5.2437e-04
Epoch 8/10
43s - loss: 5.4960e-04
Epoch 9/10
43s - loss: 5.3203e-04
Epoch 10/10
44s - loss: 4.9286e-04
Train Score: 270.96 RMSE
Test Score: 144.13 RMSE

Epoch 1/10

44s - loss: 0.0040

Epoch 2/10

44s - loss: 0.0013

Epoch 3/10

43s - loss: 0.0011

Epoch 4/10

44s - loss: 7.8079e-04

Epoch 5/10

44s - loss: 5.9064e-04

Epoch 6/10

44s - loss: 5.5586e-04

Epoch 7/10

43s - loss: 5.2437e-04

Epoch 8/10

43s - loss: 5.4960e-04

Epoch 9/10

43s - loss: 5.3203e-04

Epoch 10/10

44s - loss: 4.9286e-04

Train Score: 270.96 RMSE

Test Score: 144.13 RMSE

予想データ　predict

2017年12月31日 by 河副太智 Leave a Comment

# モデルの作成と学習
model = Sequential()#その他学習モデル

# 予測データの作成
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)

# モデルの作成と学習

model = Sequential()#その他学習モデル

# 予測データの作成

trainPredict = model.predict(trainX)

testPredict = model.predict(testX)

LSTMモデルの作成と学習

2017年12月29日 by 河副太智 Leave a Comment

# LSTMモデルの作成と学習
model = Sequential()
model.add(LSTM(64, return_sequences=True,input_shape=(look_back, 1)))
model.add(LSTM(32))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2)

# LSTMモデルの作成と学習

model = Sequential()

model.add(LSTM(64, return_sequences=True,input_shape=(look_back, 1)))

model.add(LSTM(32))

model.add(Dense(1))

model.compile(loss='mean_squared_error', optimizer='adam')

model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2)

look_backで過去に遡る

2017年12月29日 by 河副太智 Leave a Comment

それぞれのYにlook_backの値を代入

# データの作成
look_back = 10

trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)

# データの作成

look_back = 10

trainX, trainY = create_dataset(train, look_back)

testX, testY = create_dataset(test, look_back)

ユーザー定義関数を作成して実行結果を受け取る

# データセットの作成
def create_dataset(dataset, look_back):
	dataX, dataY = [], []
	for i in range(len(dataset)-look_back-1):
		a = dataset[i:(i+look_back), 0]
		dataX.append(a)
		dataY.append(dataset[i + look_back, 0])
	return numpy.array(dataX), numpy.array(dataY)

# データセットの作成

def create_dataset(dataset, look_back):

dataX, dataY = [], []

for i in range(len(dataset)-look_back-1):

a = dataset[i:(i+look_back), 0]

dataX.append(a)

dataY.append(dataset[i + look_back, 0])

return numpy.array(dataX), numpy.array(dataY)

実行前

[[ 0.02588999]
 [ 0.04530746]
 [ 0.09061491]
 [ 0.08090615]
 [ 0.05501619]
 [ 0.10032365]
 [ 0.14239484]
 [ 0.14239484]
 [ 0.10355988]
 [ 0.04854369]
 [ 0.        ]
 [ 0.04530746]
 [ 0.03559873]
 [ 0.07119742]
 [ 0.11974111]
 [ 0.10032365]
 [ 0.06796119]
 [ 0.14563107]
 [ 0.21359226]
 [ 0.21359226]
 [ 0.17475727]
 [ 0.09385115]
 [ 0.03236246]
 [ 0.11650488]
 [ 0.13268611]
 [ 0.14886734]
 [ 0.23948219]
 [ 0.1909385 ]
 [ 0.22006473]
 [ 0.23948219]
 [ 0.30744341]
 [ 0.30744341]
 [ 0.25889972]
 [ 0.18770227]
 [ 0.13592234]
 [ 0.20064726]
 [ 0.2168285 ]
 [ 0.24595472]
 [ 0.28802589]
 [ 0.24919096]
 [ 0.25566342]
 [ 0.36893204]
 [ 0.40776703]
 [ 0.44660196]
 [ 0.33980587]
 [ 0.28155342]
 [ 0.22006473]
 [ 0.29126218]
 [ 0.29773465]
 [ 0.29773465]
 [ 0.42718449]
 [ 0.42394826]
 [ 0.40453079]
 [ 0.44983819]
 [ 0.51779938]
 [ 0.54368937]
 [ 0.43042073]
 [ 0.34627834]
 [ 0.24595472]
 [ 0.31391588]
 [ 0.32362464]
 [ 0.27184466]
 [ 0.42394826]
 [ 0.39805827]
 [ 0.42071202]
 [ 0.51779938]
 [ 0.64077675]
 [ 0.61165047]
 [ 0.50161815]
 [ 0.40453079]
 [ 0.32038835]
 [ 0.40453079]
 [ 0.44660196]
 [ 0.41747573]
 [ 0.52750814]
 [ 0.53398061]
 [ 0.5372169 ]
 [ 0.68284798]
 [ 0.84142399]
 [ 0.78640783]
 [ 0.67313921]
 [ 0.55016184]
 [ 0.43042073]
 [ 0.56310678]
 [ 0.5825243 ]
 [ 0.5598706 ]
 [ 0.68932045]
 [ 0.67637551]
 [ 0.69255674]
 [ 0.87378645]
 [ 1.00000012]
 [ 0.97411013]
 [ 0.81229782]
 [ 0.65372169]
 [ 0.54045308]]

[[ 0.02588999]

[ 0.04530746]

[ 0.09061491]

[ 0.08090615]

[ 0.05501619]

[ 0.10032365]

[ 0.14239484]

[ 0.10355988]

[ 0.04854369]

[ 0. ]

[ 0.04530746]

[ 0.03559873]

[ 0.07119742]

[ 0.11974111]

[ 0.10032365]

[ 0.06796119]

[ 0.14563107]

[ 0.21359226]

[ 0.17475727]

[ 0.09385115]

[ 0.03236246]

[ 0.11650488]

[ 0.13268611]

[ 0.14886734]

[ 0.23948219]

[ 0.1909385 ]

[ 0.22006473]

[ 0.23948219]

[ 0.30744341]

[ 0.25889972]

[ 0.18770227]

[ 0.13592234]

[ 0.20064726]

[ 0.2168285 ]

[ 0.24595472]

[ 0.28802589]

[ 0.24919096]

[ 0.25566342]

[ 0.36893204]

[ 0.40776703]

[ 0.44660196]

[ 0.33980587]

[ 0.28155342]

[ 0.22006473]

[ 0.29126218]

[ 0.29773465]

[ 0.42718449]

[ 0.42394826]

[ 0.40453079]

[ 0.44983819]

[ 0.51779938]

[ 0.54368937]

[ 0.43042073]

[ 0.34627834]

[ 0.24595472]

[ 0.31391588]

[ 0.32362464]

[ 0.27184466]

[ 0.42394826]

[ 0.39805827]

[ 0.42071202]

[ 0.51779938]

[ 0.64077675]

[ 0.61165047]

[ 0.50161815]

[ 0.40453079]

[ 0.32038835]

[ 0.40453079]

[ 0.44660196]

[ 0.41747573]

[ 0.52750814]

[ 0.53398061]

[ 0.5372169 ]

[ 0.68284798]

[ 0.84142399]

[ 0.78640783]

[ 0.67313921]

[ 0.55016184]

[ 0.43042073]

[ 0.56310678]

[ 0.5825243 ]

[ 0.5598706 ]

[ 0.68932045]

[ 0.67637551]

[ 0.69255674]

[ 0.87378645]

[ 1.00000012]

[ 0.97411013]

[ 0.81229782]

[ 0.65372169]

[ 0.54045308]]

実行後

[[ 0.02588999  0.04530746  0.09061491  0.08090615  0.05501619  0.10032365
   0.14239484  0.14239484  0.10355988  0.04854369]
 [ 0.04530746  0.09061491  0.08090615  0.05501619  0.10032365  0.14239484
   0.14239484  0.10355988  0.04854369  0.        ]
 [ 0.09061491  0.08090615  0.05501619  0.10032365  0.14239484  0.14239484
   0.10355988  0.04854369  0.          0.04530746]
 [ 0.08090615  0.05501619  0.10032365  0.14239484  0.14239484  0.10355988
   0.04854369  0.          0.04530746  0.03559873]
 [ 0.05501619  0.10032365  0.14239484  0.14239484  0.10355988  0.04854369
   0.          0.04530746  0.03559873  0.07119742]
 [ 0.10032365  0.14239484  0.14239484  0.10355988  0.04854369  0.
   0.04530746  0.03559873  0.07119742  0.11974111]
 [ 0.14239484  0.14239484  0.10355988  0.04854369  0.          0.04530746
   0.03559873  0.07119742  0.11974111  0.10032365]
 [ 0.14239484  0.10355988  0.04854369  0.          0.04530746  0.03559873
   0.07119742  0.11974111  0.10032365  0.06796119]
 [ 0.10355988  0.04854369  0.          0.04530746  0.03559873  0.07119742
   0.11974111  0.10032365  0.06796119  0.14563107]
 [ 0.04854369  0.          0.04530746  0.03559873  0.07119742  0.11974111
   0.10032365  0.06796119  0.14563107  0.21359226]
 [ 0.          0.04530746  0.03559873  0.07119742  0.11974111  0.10032365
   0.06796119  0.14563107  0.21359226  0.21359226]
 [ 0.04530746  0.03559873  0.07119742  0.11974111  0.10032365  0.06796119
   0.14563107  0.21359226  0.21359226  0.17475727]
 [ 0.03559873  0.07119742  0.11974111  0.10032365  0.06796119  0.14563107
   0.21359226  0.21359226  0.17475727  0.09385115]
 [ 0.07119742  0.11974111  0.10032365  0.06796119  0.14563107  0.21359226
   0.21359226  0.17475727  0.09385115  0.03236246]
 [ 0.11974111  0.10032365  0.06796119  0.14563107  0.21359226  0.21359226
   0.17475727  0.09385115  0.03236246  0.11650488]
 [ 0.10032365  0.06796119  0.14563107  0.21359226  0.21359226  0.17475727
   0.09385115  0.03236246  0.11650488  0.13268611]
 [ 0.06796119  0.14563107  0.21359226  0.21359226  0.17475727  0.09385115
   0.03236246  0.11650488  0.13268611  0.14886734]
 [ 0.14563107  0.21359226  0.21359226  0.17475727  0.09385115  0.03236246
   0.11650488  0.13268611  0.14886734  0.23948219]
 [ 0.21359226  0.21359226  0.17475727  0.09385115  0.03236246  0.11650488
   0.13268611  0.14886734  0.23948219  0.1909385 ]
 [ 0.21359226  0.17475727  0.09385115  0.03236246  0.11650488  0.13268611
   0.14886734  0.23948219  0.1909385   0.22006473]
 [ 0.17475727  0.09385115  0.03236246  0.11650488  0.13268611  0.14886734
   0.23948219  0.1909385   0.22006473  0.23948219]
 [ 0.09385115  0.03236246  0.11650488  0.13268611  0.14886734  0.23948219
   0.1909385   0.22006473  0.23948219  0.30744341]
 [ 0.03236246  0.11650488  0.13268611  0.14886734  0.23948219  0.1909385
   0.22006473  0.23948219  0.30744341  0.30744341]
 [ 0.11650488  0.13268611  0.14886734  0.23948219  0.1909385   0.22006473
   0.23948219  0.30744341  0.30744341  0.25889972]
 [ 0.13268611  0.14886734  0.23948219  0.1909385   0.22006473  0.23948219
   0.30744341  0.30744341  0.25889972  0.18770227]
 [ 0.14886734  0.23948219  0.1909385   0.22006473  0.23948219  0.30744341
   0.30744341  0.25889972  0.18770227  0.13592234]
 [ 0.23948219  0.1909385   0.22006473  0.23948219  0.30744341  0.30744341
   0.25889972  0.18770227  0.13592234  0.20064726]
 [ 0.1909385   0.22006473  0.23948219  0.30744341  0.30744341  0.25889972
   0.18770227  0.13592234  0.20064726  0.2168285 ]
 [ 0.22006473  0.23948219  0.30744341  0.30744341  0.25889972  0.18770227
   0.13592234  0.20064726  0.2168285   0.24595472]
 [ 0.23948219  0.30744341  0.30744341  0.25889972  0.18770227  0.13592234
   0.20064726  0.2168285   0.24595472  0.28802589]
 [ 0.30744341  0.30744341  0.25889972  0.18770227  0.13592234  0.20064726
   0.2168285   0.24595472  0.28802589  0.24919096]
 [ 0.30744341  0.25889972  0.18770227  0.13592234  0.20064726  0.2168285
   0.24595472  0.28802589  0.24919096  0.25566342]
 [ 0.25889972  0.18770227  0.13592234  0.20064726  0.2168285   0.24595472
   0.28802589  0.24919096  0.25566342  0.36893204]
 [ 0.18770227  0.13592234  0.20064726  0.2168285   0.24595472  0.28802589
   0.24919096  0.25566342  0.36893204  0.40776703]
 [ 0.13592234  0.20064726  0.2168285   0.24595472  0.28802589  0.24919096
   0.25566342  0.36893204  0.40776703  0.44660196]
 [ 0.20064726  0.2168285   0.24595472  0.28802589  0.24919096  0.25566342
   0.36893204  0.40776703  0.44660196  0.33980587]
 [ 0.2168285   0.24595472  0.28802589  0.24919096  0.25566342  0.36893204
   0.40776703  0.44660196  0.33980587  0.28155342]
 [ 0.24595472  0.28802589  0.24919096  0.25566342  0.36893204  0.40776703
   0.44660196  0.33980587  0.28155342  0.22006473]
 [ 0.28802589  0.24919096  0.25566342  0.36893204  0.40776703  0.44660196
   0.33980587  0.28155342  0.22006473  0.29126218]
 [ 0.24919096  0.25566342  0.36893204  0.40776703  0.44660196  0.33980587
   0.28155342  0.22006473  0.29126218  0.29773465]
 [ 0.25566342  0.36893204  0.40776703  0.44660196  0.33980587  0.28155342
   0.22006473  0.29126218  0.29773465  0.29773465]
 [ 0.36893204  0.40776703  0.44660196  0.33980587  0.28155342  0.22006473
   0.29126218  0.29773465  0.29773465  0.42718449]
 [ 0.40776703  0.44660196  0.33980587  0.28155342  0.22006473  0.29126218
   0.29773465  0.29773465  0.42718449  0.42394826]
 [ 0.44660196  0.33980587  0.28155342  0.22006473  0.29126218  0.29773465
   0.29773465  0.42718449  0.42394826  0.40453079]
 [ 0.33980587  0.28155342  0.22006473  0.29126218  0.29773465  0.29773465
   0.42718449  0.42394826  0.40453079  0.44983819]
 [ 0.28155342  0.22006473  0.29126218  0.29773465  0.29773465  0.42718449
   0.42394826  0.40453079  0.44983819  0.51779938]
 [ 0.22006473  0.29126218  0.29773465  0.29773465  0.42718449  0.42394826
   0.40453079  0.44983819  0.51779938  0.54368937]
 [ 0.29126218  0.29773465  0.29773465  0.42718449  0.42394826  0.40453079
   0.44983819  0.51779938  0.54368937  0.43042073]
 [ 0.29773465  0.29773465  0.42718449  0.42394826  0.40453079  0.44983819
   0.51779938  0.54368937  0.43042073  0.34627834]
 [ 0.29773465  0.42718449  0.42394826  0.40453079  0.44983819  0.51779938
   0.54368937  0.43042073  0.34627834  0.24595472]
 [ 0.42718449  0.42394826  0.40453079  0.44983819  0.51779938  0.54368937
   0.43042073  0.34627834  0.24595472  0.31391588]
 [ 0.42394826  0.40453079  0.44983819  0.51779938  0.54368937  0.43042073
   0.34627834  0.24595472  0.31391588  0.32362464]
 [ 0.40453079  0.44983819  0.51779938  0.54368937  0.43042073  0.34627834
   0.24595472  0.31391588  0.32362464  0.27184466]
 [ 0.44983819  0.51779938  0.54368937  0.43042073  0.34627834  0.24595472
   0.31391588  0.32362464  0.27184466  0.42394826]
 [ 0.51779938  0.54368937  0.43042073  0.34627834  0.24595472  0.31391588
   0.32362464  0.27184466  0.42394826  0.39805827]
 [ 0.54368937  0.43042073  0.34627834  0.24595472  0.31391588  0.32362464
   0.27184466  0.42394826  0.39805827  0.42071202]
 [ 0.43042073  0.34627834  0.24595472  0.31391588  0.32362464  0.27184466
   0.42394826  0.39805827  0.42071202  0.51779938]
 [ 0.34627834  0.24595472  0.31391588  0.32362464  0.27184466  0.42394826
   0.39805827  0.42071202  0.51779938  0.64077675]
 [ 0.24595472  0.31391588  0.32362464  0.27184466  0.42394826  0.39805827
   0.42071202  0.51779938  0.64077675  0.61165047]
 [ 0.31391588  0.32362464  0.27184466  0.42394826  0.39805827  0.42071202
   0.51779938  0.64077675  0.61165047  0.50161815]
 [ 0.32362464  0.27184466  0.42394826  0.39805827  0.42071202  0.51779938
   0.64077675  0.61165047  0.50161815  0.40453079]
 [ 0.27184466  0.42394826  0.39805827  0.42071202  0.51779938  0.64077675
   0.61165047  0.50161815  0.40453079  0.32038835]
 [ 0.42394826  0.39805827  0.42071202  0.51779938  0.64077675  0.61165047
   0.50161815  0.40453079  0.32038835  0.40453079]
 [ 0.39805827  0.42071202  0.51779938  0.64077675  0.61165047  0.50161815
   0.40453079  0.32038835  0.40453079  0.44660196]
 [ 0.42071202  0.51779938  0.64077675  0.61165047  0.50161815  0.40453079
   0.32038835  0.40453079  0.44660196  0.41747573]
 [ 0.51779938  0.64077675  0.61165047  0.50161815  0.40453079  0.32038835
   0.40453079  0.44660196  0.41747573  0.52750814]
 [ 0.64077675  0.61165047  0.50161815  0.40453079  0.32038835  0.40453079
   0.44660196  0.41747573  0.52750814  0.53398061]
 [ 0.61165047  0.50161815  0.40453079  0.32038835  0.40453079  0.44660196
   0.41747573  0.52750814  0.53398061  0.5372169 ]
 [ 0.50161815  0.40453079  0.32038835  0.40453079  0.44660196  0.41747573
   0.52750814  0.53398061  0.5372169   0.68284798]
 [ 0.40453079  0.32038835  0.40453079  0.44660196  0.41747573  0.52750814
   0.53398061  0.5372169   0.68284798  0.84142399]
 [ 0.32038835  0.40453079  0.44660196  0.41747573  0.52750814  0.53398061
   0.5372169   0.68284798  0.84142399  0.78640783]
 [ 0.40453079  0.44660196  0.41747573  0.52750814  0.53398061  0.5372169
   0.68284798  0.84142399  0.78640783  0.67313921]
 [ 0.44660196  0.41747573  0.52750814  0.53398061  0.5372169   0.68284798
   0.84142399  0.78640783  0.67313921  0.55016184]
 [ 0.41747573  0.52750814  0.53398061  0.5372169   0.68284798  0.84142399
   0.78640783  0.67313921  0.55016184  0.43042073]
 [ 0.52750814  0.53398061  0.5372169   0.68284798  0.84142399  0.78640783
   0.67313921  0.55016184  0.43042073  0.56310678]
 [ 0.53398061  0.5372169   0.68284798  0.84142399  0.78640783  0.67313921
   0.55016184  0.43042073  0.56310678  0.5825243 ]
 [ 0.5372169   0.68284798  0.84142399  0.78640783  0.67313921  0.55016184
   0.43042073  0.56310678  0.5825243   0.5598706 ]
 [ 0.68284798  0.84142399  0.78640783  0.67313921  0.55016184  0.43042073
   0.56310678  0.5825243   0.5598706   0.68932045]
 [ 0.84142399  0.78640783  0.67313921  0.55016184  0.43042073  0.56310678
   0.5825243   0.5598706   0.68932045  0.67637551]
 [ 0.78640783  0.67313921  0.55016184  0.43042073  0.56310678  0.5825243
   0.5598706   0.68932045  0.67637551  0.69255674]
 [ 0.67313921  0.55016184  0.43042073  0.56310678  0.5825243   0.5598706
   0.68932045  0.67637551  0.69255674  0.87378645]
 [ 0.55016184  0.43042073  0.56310678  0.5825243   0.5598706   0.68932045
   0.67637551  0.69255674  0.87378645  1.00000012]
 [ 0.43042073  0.56310678  0.5825243   0.5598706   0.68932045  0.67637551
   0.69255674  0.87378645  1.00000012  0.97411013]
 [ 0.56310678  0.5825243   0.5598706   0.68932045  0.67637551  0.69255674
   0.87378645  1.00000012  0.97411013  0.81229782]]

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

[[ 0.02588999 0.04530746 0.09061491 0.08090615 0.05501619 0.10032365

0.14239484 0.14239484 0.10355988 0.04854369]

[ 0.04530746 0.09061491 0.08090615 0.05501619 0.10032365 0.14239484

0.14239484 0.10355988 0.04854369 0. ]

[ 0.09061491 0.08090615 0.05501619 0.10032365 0.14239484 0.14239484

0.10355988 0.04854369 0. 0.04530746]

[ 0.08090615 0.05501619 0.10032365 0.14239484 0.14239484 0.10355988

0.04854369 0. 0.04530746 0.03559873]

[ 0.05501619 0.10032365 0.14239484 0.14239484 0.10355988 0.04854369

0. 0.04530746 0.03559873 0.07119742]

[ 0.10032365 0.14239484 0.14239484 0.10355988 0.04854369 0.

0.04530746 0.03559873 0.07119742 0.11974111]

[ 0.14239484 0.14239484 0.10355988 0.04854369 0. 0.04530746

0.03559873 0.07119742 0.11974111 0.10032365]

[ 0.14239484 0.10355988 0.04854369 0. 0.04530746 0.03559873

0.07119742 0.11974111 0.10032365 0.06796119]

[ 0.10355988 0.04854369 0. 0.04530746 0.03559873 0.07119742

0.11974111 0.10032365 0.06796119 0.14563107]

[ 0.04854369 0. 0.04530746 0.03559873 0.07119742 0.11974111

0.10032365 0.06796119 0.14563107 0.21359226]

[ 0. 0.04530746 0.03559873 0.07119742 0.11974111 0.10032365

0.06796119 0.14563107 0.21359226 0.21359226]

[ 0.04530746 0.03559873 0.07119742 0.11974111 0.10032365 0.06796119

0.14563107 0.21359226 0.21359226 0.17475727]

[ 0.03559873 0.07119742 0.11974111 0.10032365 0.06796119 0.14563107

0.21359226 0.21359226 0.17475727 0.09385115]

[ 0.07119742 0.11974111 0.10032365 0.06796119 0.14563107 0.21359226

0.21359226 0.17475727 0.09385115 0.03236246]

[ 0.11974111 0.10032365 0.06796119 0.14563107 0.21359226 0.21359226

0.17475727 0.09385115 0.03236246 0.11650488]

[ 0.10032365 0.06796119 0.14563107 0.21359226 0.21359226 0.17475727

0.09385115 0.03236246 0.11650488 0.13268611]

[ 0.06796119 0.14563107 0.21359226 0.21359226 0.17475727 0.09385115

0.03236246 0.11650488 0.13268611 0.14886734]

[ 0.14563107 0.21359226 0.21359226 0.17475727 0.09385115 0.03236246

0.11650488 0.13268611 0.14886734 0.23948219]

[ 0.21359226 0.21359226 0.17475727 0.09385115 0.03236246 0.11650488

0.13268611 0.14886734 0.23948219 0.1909385 ]

[ 0.21359226 0.17475727 0.09385115 0.03236246 0.11650488 0.13268611

0.14886734 0.23948219 0.1909385 0.22006473]

[ 0.17475727 0.09385115 0.03236246 0.11650488 0.13268611 0.14886734

0.23948219 0.1909385 0.22006473 0.23948219]

[ 0.09385115 0.03236246 0.11650488 0.13268611 0.14886734 0.23948219

0.1909385 0.22006473 0.23948219 0.30744341]

[ 0.03236246 0.11650488 0.13268611 0.14886734 0.23948219 0.1909385

0.22006473 0.23948219 0.30744341 0.30744341]

[ 0.11650488 0.13268611 0.14886734 0.23948219 0.1909385 0.22006473

0.23948219 0.30744341 0.30744341 0.25889972]

[ 0.13268611 0.14886734 0.23948219 0.1909385 0.22006473 0.23948219

0.30744341 0.30744341 0.25889972 0.18770227]

[ 0.14886734 0.23948219 0.1909385 0.22006473 0.23948219 0.30744341

0.30744341 0.25889972 0.18770227 0.13592234]

[ 0.23948219 0.1909385 0.22006473 0.23948219 0.30744341 0.30744341

0.25889972 0.18770227 0.13592234 0.20064726]

[ 0.1909385 0.22006473 0.23948219 0.30744341 0.30744341 0.25889972

0.18770227 0.13592234 0.20064726 0.2168285 ]

[ 0.22006473 0.23948219 0.30744341 0.30744341 0.25889972 0.18770227

0.13592234 0.20064726 0.2168285 0.24595472]

[ 0.23948219 0.30744341 0.30744341 0.25889972 0.18770227 0.13592234

0.20064726 0.2168285 0.24595472 0.28802589]

[ 0.30744341 0.30744341 0.25889972 0.18770227 0.13592234 0.20064726

0.2168285 0.24595472 0.28802589 0.24919096]

[ 0.30744341 0.25889972 0.18770227 0.13592234 0.20064726 0.2168285

0.24595472 0.28802589 0.24919096 0.25566342]

[ 0.25889972 0.18770227 0.13592234 0.20064726 0.2168285 0.24595472

0.28802589 0.24919096 0.25566342 0.36893204]

[ 0.18770227 0.13592234 0.20064726 0.2168285 0.24595472 0.28802589

0.24919096 0.25566342 0.36893204 0.40776703]

[ 0.13592234 0.20064726 0.2168285 0.24595472 0.28802589 0.24919096

0.25566342 0.36893204 0.40776703 0.44660196]

[ 0.20064726 0.2168285 0.24595472 0.28802589 0.24919096 0.25566342

0.36893204 0.40776703 0.44660196 0.33980587]

[ 0.2168285 0.24595472 0.28802589 0.24919096 0.25566342 0.36893204

0.40776703 0.44660196 0.33980587 0.28155342]

[ 0.24595472 0.28802589 0.24919096 0.25566342 0.36893204 0.40776703

0.44660196 0.33980587 0.28155342 0.22006473]

[ 0.28802589 0.24919096 0.25566342 0.36893204 0.40776703 0.44660196

0.33980587 0.28155342 0.22006473 0.29126218]

[ 0.24919096 0.25566342 0.36893204 0.40776703 0.44660196 0.33980587

0.28155342 0.22006473 0.29126218 0.29773465]

[ 0.25566342 0.36893204 0.40776703 0.44660196 0.33980587 0.28155342

0.22006473 0.29126218 0.29773465 0.29773465]

[ 0.36893204 0.40776703 0.44660196 0.33980587 0.28155342 0.22006473

0.29126218 0.29773465 0.29773465 0.42718449]

[ 0.40776703 0.44660196 0.33980587 0.28155342 0.22006473 0.29126218

0.29773465 0.29773465 0.42718449 0.42394826]

[ 0.44660196 0.33980587 0.28155342 0.22006473 0.29126218 0.29773465

0.29773465 0.42718449 0.42394826 0.40453079]

[ 0.33980587 0.28155342 0.22006473 0.29126218 0.29773465 0.29773465

0.42718449 0.42394826 0.40453079 0.44983819]

[ 0.28155342 0.22006473 0.29126218 0.29773465 0.29773465 0.42718449

0.42394826 0.40453079 0.44983819 0.51779938]

[ 0.22006473 0.29126218 0.29773465 0.29773465 0.42718449 0.42394826

0.40453079 0.44983819 0.51779938 0.54368937]

[ 0.29126218 0.29773465 0.29773465 0.42718449 0.42394826 0.40453079

0.44983819 0.51779938 0.54368937 0.43042073]

[ 0.29773465 0.29773465 0.42718449 0.42394826 0.40453079 0.44983819

0.51779938 0.54368937 0.43042073 0.34627834]

[ 0.29773465 0.42718449 0.42394826 0.40453079 0.44983819 0.51779938

0.54368937 0.43042073 0.34627834 0.24595472]

[ 0.42718449 0.42394826 0.40453079 0.44983819 0.51779938 0.54368937

0.43042073 0.34627834 0.24595472 0.31391588]

[ 0.42394826 0.40453079 0.44983819 0.51779938 0.54368937 0.43042073

0.34627834 0.24595472 0.31391588 0.32362464]

[ 0.40453079 0.44983819 0.51779938 0.54368937 0.43042073 0.34627834

0.24595472 0.31391588 0.32362464 0.27184466]

[ 0.44983819 0.51779938 0.54368937 0.43042073 0.34627834 0.24595472

0.31391588 0.32362464 0.27184466 0.42394826]

[ 0.51779938 0.54368937 0.43042073 0.34627834 0.24595472 0.31391588

0.32362464 0.27184466 0.42394826 0.39805827]

[ 0.54368937 0.43042073 0.34627834 0.24595472 0.31391588 0.32362464

0.27184466 0.42394826 0.39805827 0.42071202]

[ 0.43042073 0.34627834 0.24595472 0.31391588 0.32362464 0.27184466

0.42394826 0.39805827 0.42071202 0.51779938]

[ 0.34627834 0.24595472 0.31391588 0.32362464 0.27184466 0.42394826

0.39805827 0.42071202 0.51779938 0.64077675]

[ 0.24595472 0.31391588 0.32362464 0.27184466 0.42394826 0.39805827

0.42071202 0.51779938 0.64077675 0.61165047]

[ 0.31391588 0.32362464 0.27184466 0.42394826 0.39805827 0.42071202

0.51779938 0.64077675 0.61165047 0.50161815]

[ 0.32362464 0.27184466 0.42394826 0.39805827 0.42071202 0.51779938

0.64077675 0.61165047 0.50161815 0.40453079]

[ 0.27184466 0.42394826 0.39805827 0.42071202 0.51779938 0.64077675

0.61165047 0.50161815 0.40453079 0.32038835]

[ 0.42394826 0.39805827 0.42071202 0.51779938 0.64077675 0.61165047

0.50161815 0.40453079 0.32038835 0.40453079]

[ 0.39805827 0.42071202 0.51779938 0.64077675 0.61165047 0.50161815

0.40453079 0.32038835 0.40453079 0.44660196]

[ 0.42071202 0.51779938 0.64077675 0.61165047 0.50161815 0.40453079

0.32038835 0.40453079 0.44660196 0.41747573]

[ 0.51779938 0.64077675 0.61165047 0.50161815 0.40453079 0.32038835

0.40453079 0.44660196 0.41747573 0.52750814]

[ 0.64077675 0.61165047 0.50161815 0.40453079 0.32038835 0.40453079

0.44660196 0.41747573 0.52750814 0.53398061]

[ 0.61165047 0.50161815 0.40453079 0.32038835 0.40453079 0.44660196

0.41747573 0.52750814 0.53398061 0.5372169 ]

[ 0.50161815 0.40453079 0.32038835 0.40453079 0.44660196 0.41747573

0.52750814 0.53398061 0.5372169 0.68284798]

[ 0.40453079 0.32038835 0.40453079 0.44660196 0.41747573 0.52750814

0.53398061 0.5372169 0.68284798 0.84142399]

[ 0.32038835 0.40453079 0.44660196 0.41747573 0.52750814 0.53398061

0.5372169 0.68284798 0.84142399 0.78640783]

[ 0.40453079 0.44660196 0.41747573 0.52750814 0.53398061 0.5372169

0.68284798 0.84142399 0.78640783 0.67313921]

[ 0.44660196 0.41747573 0.52750814 0.53398061 0.5372169 0.68284798

0.84142399 0.78640783 0.67313921 0.55016184]

[ 0.41747573 0.52750814 0.53398061 0.5372169 0.68284798 0.84142399

0.78640783 0.67313921 0.55016184 0.43042073]

[ 0.52750814 0.53398061 0.5372169 0.68284798 0.84142399 0.78640783

0.67313921 0.55016184 0.43042073 0.56310678]

[ 0.53398061 0.5372169 0.68284798 0.84142399 0.78640783 0.67313921

0.55016184 0.43042073 0.56310678 0.5825243 ]

[ 0.5372169 0.68284798 0.84142399 0.78640783 0.67313921 0.55016184

0.43042073 0.56310678 0.5825243 0.5598706 ]

[ 0.68284798 0.84142399 0.78640783 0.67313921 0.55016184 0.43042073

0.56310678 0.5825243 0.5598706 0.68932045]

[ 0.84142399 0.78640783 0.67313921 0.55016184 0.43042073 0.56310678

0.5825243 0.5598706 0.68932045 0.67637551]

[ 0.78640783 0.67313921 0.55016184 0.43042073 0.56310678 0.5825243

0.5598706 0.68932045 0.67637551 0.69255674]

[ 0.67313921 0.55016184 0.43042073 0.56310678 0.5825243 0.5598706

0.68932045 0.67637551 0.69255674 0.87378645]

[ 0.55016184 0.43042073 0.56310678 0.5825243 0.5598706 0.68932045

0.67637551 0.69255674 0.87378645 1.00000012]

[ 0.43042073 0.56310678 0.5825243 0.5598706 0.68932045 0.67637551

0.69255674 0.87378645 1.00000012 0.97411013]

[ 0.56310678 0.5825243 0.5598706 0.68932045 0.67637551 0.69255674

0.87378645 1.00000012 0.97411013 0.81229782]]

リアルタイム描写

2017年12月28日 by 河副太智 Leave a Comment

動くグラフ

https://www.reddit.com/r/dataisbeautiful/comments/7l9ef7/i_simulated_and_animated_500_instances_of_the/

from matplotlib import pyplot as plt
import numpy as np
import time
from IPython.display import display, clear_output
%matplotlib inline
fig = plt.figure(figsize=(16,10))
axe = fig.add_subplot(111)

# 繰り返す回数を決める
num = 5

for i in range(num):
    A = np.random.randn(1000)
    axe.hist(A,bins=50)
    axe.set_xlim([-4,4])
    axe.set_ylim([0,70])
    display(fig)
    clear_output(wait = True)
    # 最後の出力は消さないので、if文で最後だけ消さないようにする。
    if i != num-1:
        axe.cla()

from matplotlib import pyplot as plt

import numpy as np

import time

from IPython.display import display, clear_output

%matplotlib inline

fig = plt.figure(figsize=(16,10))

axe = fig.add_subplot(111)

# 繰り返す回数を決める

num = 5

for i in range(num):

A = np.random.randn(1000)

axe.hist(A,bins=50)

axe.set_xlim([-4,4])

axe.set_ylim([0,70])

display(fig)

clear_output(wait = True)

# 最後の出力は消さないので、if文で最後だけ消さないようにする。

if i != num-1:

axe.cla()

from IPython.display import display, clear_output

display(fig) 画像を出力します。
clear_output(wait = True)　出力結果を消します。
axeに入れられたグラフデータを削除します。

予想結果の可視化

2017年12月28日 by 河副太智 Leave a Comment

# プロットのためのデータ整形
trainPredictPlot = numpy.empty_like(dataset)
trainPredictPlot[:, :] = numpy.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
testPredictPlot = numpy.empty_like(dataset)
testPredictPlot[:, :] = numpy.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# テストデータのプロット
plt.plot(dataframe[round(len(dataset)*0.67):])
plt.plot(testPredictPlot)
plt.show()

# プロットのためのデータ整形

trainPredictPlot = numpy.empty_like(dataset)

trainPredictPlot[:, :] = numpy.nan

trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict

testPredictPlot = numpy.empty_like(dataset)

testPredictPlot[:, :] = numpy.nan

testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict

# テストデータのプロット

plt.plot(dataframe[round(len(dataset)*0.67):])

plt.plot(testPredictPlot)

plt.show()