class NeuralNetworkClassifier(object):
"Multi Layer Neural Network Classifier"
def __init__(self, num_nodes_list):
self.network = None
self.num_nodes_list = num_nodes_list
def train_all(self, data_table):
num_inputs = len(data_table[0]) - 1 # minus one for the answer
data_cols = np.rot90(data_table, -1)
self.network = MultiLayerNetwork(num_inputs, self.num_nodes_list)
normalized_data_cols = map(stats.zscore_map, data_cols)
normalized_data = np.rot90(normalized_data_cols, 1)
# run stuff through, and change the weights if it's wrong
def predict(self, row):
# could return multiple answers it thinks are right
answers = self.network.activate(row)
# Pick the "rightest" one
return np.argmax(answers)
def predict_all(self, data_table):
return map(predict, data_table)
def train_all(self, data_table):
num_inputs = len(data_table[0]) - 1 # minus one for the answer
data_cols = np.rot90(data_table, -1)
self.network = MultiLayerNetwork(num_inputs, self.num_nodes_list)
normalized_data_cols = map(stats.zscore_map, data_cols)
normalized_data = np.rot90(normalized_data_cols, 1)
