def __init__(self, config): LearningAlgorithm.__init__(self, [config, config.logistic]) self.iterations = config.algorithm.iterations self.training_set_data = np.array(self.training_set.data) self.training_set_labels = np.array(self.training_set.labels) self.classifiers = {} for label in set(self.training_set.labels): self.logger.debug("Creating classifier for: "+label) classifier = LogisticRegression.Classifier(config, self.training_set_data, self.training_set_labels == label) self.classifiers[label] = classifier
def __init__(self, config): LearningAlgorithm.__init__(self, [config, config.nn]) self.hidden_size = config.nn.hidden_size self.momentum =config.nn.momentum self.mini_batch_size = config.nn.mini_batch_size # Random initialization of weights self.input_to_hidden = np.random.rand(self.input_size, self.hidden_size)*2*config.nn.epsilon_init - config.nn.epsilon_init self.hidden_to_output = np.random.rand(self.hidden_size, self.output_size)*2*config.nn.epsilon_init - config.nn.epsilon_init #Initialize speed matrices self.speed = type('Speed', (object,), {'input_to_hidden': None, 'hidden_to_output': None}) self.speed.input_to_hidden = np.zeros((self.input_size, self.hidden_size)) self.speed.hidden_to_output = np.zeros((self.hidden_size, self.output_size)) self.iterations = config.algorithm.iterations
def __init__(self, config, training_set_data, training_set_labels): LearningAlgorithm.__init__(self, [config, config.logistic]) self.training_set_data = training_set_data self.training_set_labels = training_set_labels self.theta = np.zeros(self.input_size)