def main():
#initialize network
number_of_features, number_of_layers, number_of_nodes, function, number_of_classes, learning_rate, momentum, beta, fold, epoch = getInput(
)
arr_input_nodes = init.createInputNodes(number_of_features)
arr_hidden_layers = init.createHiddenLayers(number_of_features,
number_of_layers,
number_of_nodes,
number_of_classes)
arr_hidden_layers_new = init.createHiddenLayers(number_of_features,
number_of_layers,
number_of_nodes,
number_of_classes)
arr_hidden_layers_template = init.createHiddenLayers(
number_of_features, number_of_layers, number_of_nodes,
number_of_classes)
arr_Y = init.createY(number_of_nodes, number_of_layers)
arr_weight_bias, arr_bias = init.createBias(number_of_nodes,
number_of_layers)
arr_weight_bias_new, arr_bias_output_new = init.createBias(
number_of_nodes, number_of_layers)
arr_weight_bias_template, arr_bias_output_template = init.createBias(
number_of_nodes, number_of_layers)
arr_output_nodes = init.createOutputNodes(number_of_classes)
arr_weight_bias_output, arr_bias_output = init.createBias(
number_of_classes, 1)
arr_weight_bias_output_new, arr_bias_output_new = init.createBias(
number_of_classes, 1)
arr_weight_bias_output_template, arr_bias_output_template = init.createBias(
number_of_classes, 1)
arr_grad_output = init.createLocalGradOutput(number_of_classes)
arr_grad_hidden = init.createLocalGradHidden(number_of_nodes,
number_of_layers)
input_file = "cross-pat-input.csv"
output_file = "cross-pat-output.csv"
data_file = "cross-pat.csv"
cv.crossValidation(input_file, output_file, data_file, fold, arr_input_nodes, arr_hidden_layers, arr_hidden_layers_new, arr_hidden_layers_template, \
arr_Y, arr_output_nodes, arr_weight_bias, arr_bias, arr_weight_bias_output, arr_bias_output, function, momentum, learning_rate, beta, arr_grad_hidden, arr_grad_output,\
number_of_features, number_of_layers, number_of_nodes, number_of_classes, epoch, arr_weight_bias_template, arr_weight_bias_output_template, arr_weight_bias_new, \
arr_weight_bias_output_new)
print("size of list containing hidden layer : " +
str(len(arr_hidden_layers)))
print(
str(len(arr_hidden_layers[1])) +
" layer(s) of weigh connected to hidden node")
print("1 layer of weight connected to INPUT layer")
print("1 layer connected to OUTPUT layer")
print("total layer of weight : " + str(1 + len(arr_hidden_layers)))
def test4(origin_feature,kmeans_feature,combine_feature): print "Approach A (Origin Feature):" cross.crossValidation(origin_feature) print "Apporach B (kmans):" cross.crossValidation(kmeans_feature) print "Aapproach C (combine):" cross.crossValidation(combine_feature)
def cross_validate(self):
crossValidation(self)
def test3(origin_feature,kmeans_feature): print "Approach A (Origin Feature):" cross.crossValidation(origin_feature) print "Apporach B (kmans):" cross.crossValidation(kmeans_feature)
def test2(kmeans_feature,smeans_feature): print "Approach A (Kmeans):" cross.crossValidation(kmeans_feature) print "Approach B (Spheric means):" cross.crossValidation(smeans_feature)