from keras.models import Sequential from keras.layers import Dense, Activation from sklearn.metrics import roc_curve from pca import PCA import matplotlib.pyplot as plt import numpy as np training_data = IciData('/home/simonpf/projects/ici/data/sets/full/train.nc') test_data = IciData('/home/simonpf/projects/ici/data/sets/full/test.nc') # Load data and perform SVD x_train = training_data.get_input_data() x_test = test_data.get_input_data() u,s,v = np.linalg.svd(x_train, full_matrices=0) pca = PCA.fromRMatrix(v) x_train_pca = pca.apply(x_train) x_test_pca = pca.apply(x_test) y_train = training_data.get_output_data("clear_sky") y_test = test_data.get_output_data("clear_sky") ## Set up train deep models. #model_deep = Sequential() #model_deep.add(Dense(input_dim = 11, units = 32)) #model_deep.add(Activation('relu')) #model_deep.add(Dense( units = 32)) #model_deep.add(Activation('relu')) #model_deep.add(Dense( units = 32)) #model_deep.add(Activation('relu')) #model_deep.add(Dense( units = 32)) #model_deep.add(Activation('relu'))
def test_fromRMatrix(self): pca = PCA.fromRMatrix(self.u) self.assertTrue(np.all(pca.u == self.u))