From b56b74b3036793ecaf61063eb0fb56ecfbd001c6 Mon Sep 17 00:00:00 2001 From: Petra Date: Mon, 8 Apr 2019 15:32:38 +0200 Subject: [PATCH] Delete 9_neural_nets-4-time_series_Metod.py --- 9_neural_nets-4-time_series_Metod.py | 107 --------------------------- 1 file changed, 107 deletions(-) delete mode 100644 9_neural_nets-4-time_series_Metod.py diff --git a/9_neural_nets-4-time_series_Metod.py b/9_neural_nets-4-time_series_Metod.py deleted file mode 100644 index 31ff357..0000000 --- a/9_neural_nets-4-time_series_Metod.py +++ /dev/null @@ -1,107 +0,0 @@ -from pandas import read_csv -from matplotlib import pyplot -from sklearn.preprocessing import LabelEncoder -from sklearn.preprocessing import MinMaxScaler -from pandas import DataFrame -from pandas import concat -from keras import Sequential -from keras.layers import Dense -from keras.layers import LSTM -from sklearn.metrics import mean_squared_error -from numpy import concatenate -from math import sqrt - -#load and plot dataset -#file = r'd:\tmp\technical_analysis_test_eth_usd_bitstamp_900_2018-12-01T00-00-00_2019-02-15T00-00-00_5minfuture.csv' -file = r"d:\tmp\technical_tmp.csv " - -# load dataset -dataset = read_csv(file , header=0, index_col=0) -values = dataset.values -# specify columns to plot -groups = range(len(dataset.columns)) -i = 1 -# plot each column -pyplot.figure() -for group in groups: - pyplot.subplot(len(groups), 1, i) - pyplot.plot(values[:, group]) - pyplot.title(dataset.columns[group], y=0.5, loc='right') - i += 1 -pyplot.show() - -#--------------------------------------------------- -# convert series to supervised learning - -print (dataset.head) - -# load dataset -#dataset = read_csv('.\Datasets\pollution_clean.csv', header=0, index_col=0) -#values = dataset.values -# ensure all data is float -values = values.astype('float32') -# normalize features -scaler = MinMaxScaler(feature_range=(0, 1)) - - -scaled = scaler.fit_transform(values) -print ("shape ", scaled.shape) - - -# drop columns we don't want to predict - - -# split into train and test sets -#values = reframed.values -n_train_hours = 5000 -train = values[:n_train_hours, :] -print ("tainn ", train.shape) -test = values[n_train_hours:, :] -print ("test ", test.shape) -# split into input and outputs -train_X, train_y = train[:, :-1], train[:, -1] -test_X, test_y = test[:, :-1], test[:, -1] -# reshape input to be 3D [samples, timesteps, features] -train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1])) -test_X = test_X.reshape((test_X.shape[0], 1, test_X.shape[1])) -print(train_X.shape, train_y.shape, test_X.shape, test_y.shape) - -# design network -model = Sequential() -model.add(LSTM(50, input_shape=(train_X.shape[1], train_X.shape[2]))) -model.add(Dense(1)) -model.compile(loss='mae', optimizer='adam') -# fit network -history = model.fit(train_X, train_y, epochs=50, batch_size=72, validation_data=(test_X, test_y), verbose=2, shuffle=False) -# plot history -pyplot.plot(history.history['loss'], label='train') -pyplot.plot(history.history['val_loss'], label='test') -pyplot.legend() -pyplot.show() - -print ("make prediction") -# make a prediction -yhat = model.predict(test_X) -print ("yhat", yhat.shape) -test_X = test_X.reshape((test_X.shape[0], test_X.shape[2])) -print ("test_X", test_X.shape) - -# invert scaling for actual -#test_y = test_y.reshape((len(test_y), 1)) -inv_y = concatenate((test_y, test_X[:, 1:]), axis=1) -print ("inv_y", inv_y.shape) -inv_y = scaler.inverse_transform(inv_y) -inv_y = inv_y[:,0] - - - -# invert scaling for forecast -inv_yhat = concatenate((yhat, test_X[:, 1:]), axis=1) -print ("inv_yhat conc", inv_yhat.shape) -inv_yhat = scaler.inverse_transform(inv_yhat) -inv_yhat = inv_yhat[:,0] - - -# calculate RMSE -rmse = sqrt(mean_squared_error(inv_y, inv_yhat)) -print('Test RMSE: %.3f' % rmse) \ No newline at end of file -- GitLab