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- from pandas import read_csv
- from sklearn.neural_network import MLPRegressor
- from sklearn.metrics import mean_squared_error
- from sklearn.model_selection import train_test_split, cross_val_score, validation_curve
- import numpy as np
- import matplotlib.pyplot as plt
- data = np.array(read_csv('timeseries_8_2.csv', index_col=0))
- inputs = data[:, :8]
- targets = data[:, 8:]
- x_train, x_test, y_train, y_test = train_test_split(
- inputs, targets, test_size=0.1, random_state=2)
- rate1 = 0.005
- rate2 = 0.1
- mlpr = MLPRegressor(hidden_layer_sizes=(12,10), max_iter=700, learning_rate_init=rate1)
- # trained = mlpr.fit(x_train, y_train) # should I fit before cross val?
- # predicted = mlpr.predict(x_test)
- scores = cross_val_score(mlpr, inputs, targets, cv=5)
- print(scores)
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