def next_sample(self, batch_size=1):
""" Return batch_size samples generated by choosing a centroid at
random and randomly offsetting its attributes so that it is
placed inside the hypersphere of that centroid.
Parameters
----------
batch_size: int
The number of samples to return.
Returns
-------
tuple or tuple list
Return a tuple with the features matrix and the labels matrix for
the batch_size samples that were requested.
"""
data = np.zeros([batch_size, self.n_features + 1])
for j in range(batch_size):
centroid_aux = self.centroids[prp.random_index_based_on_weights(self.centroid_weights,
self._sample_random_state)]
att_vals = []
magnitude = 0.0
for i in range(self.n_features):
att_vals.append((self._sample_random_state.rand() * 2.0) - 1.0)
magnitude += att_vals[i] * att_vals[i]
magnitude = np.sqrt(magnitude)
desired_mag = self._sample_random_state.normal() * centroid_aux.std_dev
scale = desired_mag / magnitude
for i in range(self.n_features):
if i < self.n_not_redund_features :
data[j, i] = centroid_aux.centre[i] + att_vals[i] * scale
else :
# data[j, i] = math.sqrt(abs(data[j, self.index_redund[i-self.n_not_redund_features]] * self.coef_redund[i-self.n_not_redund_features])) # Add redundant feats
data[j, i] = data[j, self.index_redund[i-self.n_not_redund_features]] # Add redundant feats
data[j, self.n_features] = centroid_aux.class_label
# Add noise
model_random_state = check_random_state(self.model_random_state)
if 0.01 + model_random_state.rand() <= self.noise_percentage:
new_class = model_random_state.randint(self.n_classes)
while(new_class == data[j, self.n_features]) :
new_class = model_random_state.randint(self.n_classes)
data[j, self.n_features] = new_class
self.current_sample_x = data[:, :self.n_features]
self.current_sample_y = data[:, self.n_features:].flatten().astype(int)
return self.current_sample_x, self.current_sample_y
def next_sample(self, batch_size=1):
""" Returns next sample from the stream.
Return batch_size samples generated by choosing a centroid at
random and randomly offsetting its attributes so that it is
placed inside the hypersphere of that centroid.
Parameters
----------
batch_size: int (optional, default=1)
The number of samples to return.
Returns
-------
tuple or tuple list
Return a tuple with the features matrix and the labels matrix for
the batch_size samples that were requested.
"""
data = np.zeros([batch_size, self.n_features + 1])
for j in range(batch_size):
centroid_aux = self.centroids[prp.random_index_based_on_weights(
self.centroid_weights, self._sample_random_state)]
att_vals = []
magnitude = 0.0
for i in range(self.n_features):
att_vals.append((self._sample_random_state.rand() * 2.0) - 1.0)
magnitude += att_vals[i] * att_vals[i]
magnitude = np.sqrt(magnitude)
desired_mag = self._sample_random_state.normal(
) * centroid_aux.std_dev
scale = desired_mag / magnitude
for i in range(self.n_features):
data[j, i] = centroid_aux.centre[i] + att_vals[i] * scale
data[j, self.n_features] = centroid_aux.class_label
self.current_sample_x = data[:, :self.n_features]
self.current_sample_y = data[:, self.n_features:].flatten().astype(int)
return self.current_sample_x, self.current_sample_y