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))
