x1 = datasets[source]
x = numpy.array(x1[x_months:])
x_all = numpy.array(x1)
y1 = datasets['lai']
y = numpy.array(y1[x_months:])
# y_all = numpy.array(y1)
# We can rewrite the line equation as y = Ap,
# where A = [[x 1]] and p = [[m], [c]].
# Now use lstsq to solve for p:
A = numpy.vstack([x, numpy.ones(len(x))]).T # [[x 1]]
lin_answer = numpy.linalg.lstsq(A, y, rcond=None)
m, c = lin_answer[0]
log.info(f'm {m}, c: {c}')
y_pred = c + m * x_all
calc_rmse(y, y_pred[x_months:])
datasets[f'pred_{ds_var}'] = y_pred
predictive_models.plot(timestamps, datasets)
if __name__ == '__main__':
# load hdf5 measurement data.
timestamps, datasets = load_data()
# calculate_moving_mean()
# solver_function(datasets)
solver_function_v2(datasets)
'FCN', 'FCN_bilstm', 'FCN_bilstm_separated', 'bilstm_FCN', 'resnet',
'resnet_bilstm', 'resnet_bilstm_separated', 'bilstm_resnet', 'cnn',
'cnn_lstm', 'cnn_bilstm', 'cnn_lstm_separated', 'cnn_lstm_separated2',
'multi_cnn', 'multi_cnn_lstm', 'multi_cnn_bilstm', 'multi_cnn_bilstm2',
'lstm_cnn'
]
"""
Load data
"""
# Hyperparameters
batch_size = 64 #min(X_train.shape[0]/10, 16)
epochs = 1500
dimensions = 3 #set en 3 o 4, dependiendo de la red neuronal a aplicar,
# si la primera capa es conv1d aplicar 3, si es conv2d, aplicar 4
for j in range(len(datasets)):
X, y = load_datasets.load_data(direc, datasets[j], dimensions)
print(X.shape)
print(y.shape)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.3, random_state=42, stratify=y)
i = 1
red = 'bilstm_resnet'
standarize = True
rescale = False
normalize = False
preprocesing = False
# Procesamiento de los datos
if standarize == True:
X_train1, X_test1 = load_datasets.standardize_data(X_train, X_test)
def main():
# load hdf5 measurement data.
timestamps, datasets = load_data()
calculate_corr(datasets)
def main():
timestamps, datasets = load_data(conf['groupname'])
# calculate_moving_mean()
make_prediction(datasets)
plot(timestamps, datasets)
from convolutional_network import CNN
from load_datasets import load_data
import sys
custom_datasets = load_data('custom')
canfar_datasets = load_data('canfar-100')
batch_size = 4
print 'Initializing the CNN'
convnet = CNN(datasets=canfar_datasets, batch_size=batch_size)
epochs = 10
print 'Training the CNN for ' + str(epochs) + ' epochs'
convnet.train(epochs)
test_set_x, test_set_y = canfar_datasets[2]
test_score = convnet.test(test_set_x, test_set_y, 500)
classify_result = convnet.classify(test_set_x, batch_size)
classify_result.reshape(10000, )
print 'Test Score Result:'
print test_score
from convolutional_network import CNN
from load_datasets import load_data
import sys
custom_datasets = load_data('custom')
canfar_datasets = load_data('canfar-100')
batch_size = 4
print 'Initializing the CNN'
convnet = CNN(datasets=canfar_datasets, batch_size=batch_size)
epochs = 10
print 'Training the CNN for ' + str(epochs) + ' epochs'
convnet.train(epochs)
test_set_x, test_set_y = canfar_datasets[2]
test_score = convnet.test(test_set_x, test_set_y, 500)
classify_result = convnet.classify(test_set_x, batch_size)
classify_result.reshape(10000,)
print 'Test Score Result:'
print test_score