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


def create_dataset(dataset, look_back=1):
	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)
numpy.random.seed(7)
dataframe = pd.read_csv('removedspike1a.csv', usecols=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25], engine='python')
dataframe = dataframe.drop(dataframe.index[0])
dataframe[dataframe < 0] = numpy.nan
dataframe = dataframe.replace(to_replace=numpy.nan, value=0, method='pad')
dataset = dataframe.values
dataset = dataset.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
model = Sequential()
model.add(LSTM(200, input_shape=(1, look_back)))
model.add(Dropout(12))
model.add(Dense(1, activation='relu'))
optimizers = adam(lr=0.5, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(loss='mean_squared_error', optimizer=optimizers)
model.fit(trainX, trainY, epochs=10, batch_size=5, verbose=2)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
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))
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
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

86243a6840d40736cc57794bb5c9db10dcc875ab
<mask token>


def create_dataset(dataset, look_back=1):
    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)


<mask token>

<mask token>


def create_dataset(dataset, look_back=1):
    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)


numpy.random.seed(7)
<mask token>
model.add(LSTM(200, input_shape=(1, look_back)))
model.add(Dropout(12))
model.add(Dense(1, activation='relu'))
<mask token>
model.compile(loss='mean_squared_error', optimizer=optimizers)
model.fit(trainX, trainY, epochs=10, batch_size=5, verbose=2)
<mask token>
print('Train Score: %.2f RMSE' % trainScore)
<mask token>
print('Test Score: %.2f RMSE' % testScore)
<mask token>
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

<mask token>


def create_dataset(dataset, look_back=1):
    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)


numpy.random.seed(7)
dataframe = pd.read_csv('removedspike1a.csv', usecols=[1, 2, 3, 4, 5, 6, 7,
    8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25],
    engine='python')
dataframe = dataframe.drop(dataframe.index[0])
dataframe[dataframe < 0] = numpy.nan
dataframe = dataframe.replace(to_replace=numpy.nan, value=0, method='pad')
dataset = dataframe.values
dataset = dataset.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
model = Sequential()
model.add(LSTM(200, input_shape=(1, look_back)))
model.add(Dropout(12))
model.add(Dense(1, activation='relu'))
optimizers = adam(lr=0.5, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0,
    amsgrad=False)
model.compile(loss='mean_squared_error', optimizer=optimizers)
model.fit(trainX, trainY, epochs=10, batch_size=5, verbose=2)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
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)
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
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

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


def create_dataset(dataset, look_back=1):
    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)


numpy.random.seed(7)
dataframe = pd.read_csv('removedspike1a.csv', usecols=[1, 2, 3, 4, 5, 6, 7,
    8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25],
    engine='python')
dataframe = dataframe.drop(dataframe.index[0])
dataframe[dataframe < 0] = numpy.nan
dataframe = dataframe.replace(to_replace=numpy.nan, value=0, method='pad')
dataset = dataframe.values
dataset = dataset.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
model = Sequential()
model.add(LSTM(200, input_shape=(1, look_back)))
model.add(Dropout(12))
model.add(Dense(1, activation='relu'))
optimizers = adam(lr=0.5, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0,
    amsgrad=False)
model.compile(loss='mean_squared_error', optimizer=optimizers)
model.fit(trainX, trainY, epochs=10, batch_size=5, verbose=2)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
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)
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
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()

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


def create_dataset(dataset, look_back=1):
	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)
numpy.random.seed(7)
dataframe = pd.read_csv('removedspike1a.csv', usecols=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25], engine='python')
dataframe = dataframe.drop(dataframe.index[0])
dataframe[dataframe < 0] = numpy.nan
dataframe = dataframe.replace(to_replace=numpy.nan, value=0, method='pad')
dataset = dataframe.values
dataset = dataset.astype('float32')
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
train_size = int(len(dataset) * 0.67)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
model = Sequential()
model.add(LSTM(200, input_shape=(1, look_back)))
model.add(Dropout(12))
model.add(Dense(1, activation='relu'))
optimizers = adam(lr=0.5, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
model.compile(loss='mean_squared_error', optimizer=optimizers)
model.fit(trainX, trainY, epochs=10, batch_size=5, verbose=2)
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
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))
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
plt.plot(scaler.inverse_transform(dataset))
plt.plot(trainPredictPlot)
plt.plot(testPredictPlot)
plt.show()
