def predict_extended(self, inputs):
"""
Predicts targets for given data set.
@param data_set: data Set inheriting AbstractDataSet
:return: List of predictions, i.e. output of this net for each
observation in the data set.
"""
data_set = NumericalDataSet(inputs)
predictions = []
# loop through dataset
for observation, _ in data_set.gen_observations():
# make sure it is a numpy array
input_arr = np.array(observation)
outputs = self.feedforward(input_arr)
predictions.append(outputs[-1])
return predictions
def predict_extended(self, inputs):
"""
Predicts targets for given data set.
@param data_set: data Set inheriting AbstractDataSet
:return: List of predictions, i.e. output of this net for each
observation in the data set.
"""
data_set = NumericalDataSet(inputs)
predictions = []
# loop through dataset
for observation, _ in data_set.gen_observations( ):
# make sure it is a numpy array
input_arr = np.array(observation)
outputs = self.feedforward(input_arr)
predictions.append(outputs[-1])
return predictions
def test_gen_observations(self):
'''
Test generator getting all observations as (1,x) numpy array from input dataset
'''
inputs = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3],
[4, 4, 4]])
labels = np.array([[0], [1], [2], [3], [4]])
dataSet = NumericalDataSet(inputs, labels)
i = 0
for inputs, labels in dataSet.gen_observations():
assert_equal(inputs, i * np.ones((1, 3)),
'wrong input at observation %d' % i)
assert_equal(labels, i * np.ones((1, 1)),
'wrong label at observation %d' % i)
i = i + 1