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Prototype2 #19

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3032b07
try to fix github (develop branch is destroyed)
thatchayut Oct 1, 2018
f467e42
Merge pull request #18 from thatchayut/feature/confusion_matrix
thatchayut Oct 1, 2018
d6c130b
nothing
thatchayut Oct 7, 2018
d8afc78
complete version
thatchayut Oct 7, 2018
00a0bad
finish this branch
thatchayut Oct 7, 2018
8e6119d
nothing
thatchayut Oct 7, 2018
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Binary file modified __pycache__/cross_validation.cpython-36.pyc
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Binary file modified __pycache__/function.cpython-36.pyc
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328 changes: 100 additions & 228 deletions cross_validation.py
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19 changes: 0 additions & 19 deletions function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,22 +4,3 @@ def sigmoid(x):
return 1 / (1 + math.exp(-x))
def hyperbolicTangent(x):
return math.tanh(x/2)

def unitStep(x,beta):
if (x >= beta):
y = 1
return y
elif (x < beta):
y = -1
return y

def ramp(x,beta):
if(x >= beta):
y = 1
return y
elif(-beta < x < beta):
y = x
return y
elif(x <= -beta):
y = -1
return y
9 changes: 0 additions & 9 deletions init_node.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,7 +35,6 @@ def createHiddenLayers(number_of_features,number_of_layers,number_of_nodes,numbe
node.clear()
final.append(layer)

# final.append(layer)
#The last hidden layer connected to an output layer
count = 0
while count < int(number_of_nodes):
Expand All @@ -57,16 +56,11 @@ def createBias(number_of_nodes,number_of_layers):
for layer_count in range(0,int(number_of_layers)):
arr = np.random.uniform(low=-1.0,high=1.0,size=int(number_of_nodes))
weight_bias.append(arr)
# weight_bias.append(layer1)

#initial bias as 1
for layer_count in range(0,int(number_of_layers)):
arr = np.ones(int(number_of_nodes))
bias.append(arr)
# bias.append(layer2)
# print(weight_bias)
# print()
# print(bias)
return weight_bias, bias


Expand All @@ -77,7 +71,6 @@ def createY(number_of_nodes, number_of_layers):
for layer_count in range(0, int(number_of_layers)):
arr = np.zeros(int(number_of_nodes))
layer.append(arr)
# final.append(layer)
return layer

def createLocalGradOutput(number_of_classes):
Expand All @@ -87,9 +80,7 @@ def createLocalGradOutput(number_of_classes):
def createLocalGradHidden(number_of_nodes, number_of_layers):
node = []
arr_grad_hidden = []
# final = []
for layer_count in range(0, int(number_of_layers)):
arr = np.zeros(int(number_of_nodes))
arr_grad_hidden.append(arr)
# final.append(layer)
return arr_grad_hidden
31 changes: 6 additions & 25 deletions mlp.py
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Expand Up @@ -59,8 +59,6 @@ def main():
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_hidden_layers_new = copy.deepcopy(arr_hidden_layers)
# arr_hidden_layers_template = copy.deepcopy(arr_hidden_layers)
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)
Expand All @@ -71,37 +69,20 @@ def main():
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)
cv.crossValidation("flood-input.csv", "flood-output.csv", "flood-data.csv", fold, arr_input_nodes, arr_hidden_layers, arr_hidden_layers_new, arr_hidden_layers_template, \

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("arr_hidden_layers : ")
# print(arr_hidden_layers)
# print("arr_hidden_layers_new : ")
# print(arr_hidden_layers_new)
# print("arr_hidden_layers_template : ")
# print(arr_hidden_layers_template)
# print()

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)))
#FOR DEBUGGING!!!
# print("all layer : " + str(len(arr_hidden_layers)))
# print("hidden : " + str(len(arr_hidden_layers[1])))
# print("member in hidden : " + str(len(arr_hidden_layers[1][0])))
# print(arr_Y)
print("arr_weight_bias : " + str(arr_weight_bias))
print("arr_bias : " + str(arr_bias))
print("arr_weight_bias_output : " + str(arr_weight_bias_output))
print("arr_bias_output : " + str(arr_bias_output))
# print("arr_weight_bias : " + str(arr_weight_bias))
# print("arr_bias : " + str(arr_bias))
# print("arr_grad_output : " + str(arr_grad_output))
# print("arr_grad_hidden : " + str(arr_grad_hidden))
# print()


if __name__ == '__main__':
main()