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Classification.py
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Classification.py
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import math
import neuralgas
import numpy as np
import matplotlib.pyplot as plt
import csv
from scipy.spatial import distance
learning_coeff = 0.1
momentum_coeff = 0.2
class Classification(object):
def __init__(self, number_of_radial, number_of_linear, number_of_class, input_data_file, is_bias=0,
is_derivative=0):
np.random.seed(0)
self.radial_layer_weights = []
self.linear_layer_weights = []
self.number_of_class = number_of_class
self.delta_weights_linear_layer = []
self.delta_weights_radial_layer = []
self.delta_coefficient_radial_layer = []
self.number_of_radial = number_of_radial
self.number_of_linear = number_of_linear
self.is_bias = is_bias
self.is_derivative = is_derivative
self.input_data, self.expected_data = self.file_input(input_data_file)
self.initialze_weights()
self.radial_coefficient = []
self.set_radial_coefficient()
self.epoch_error = 0.0
self.error_for_epoch = []
self.epoch_for_error = []
def initialze_weights(self):
input = np.copy(self.input_data)
SOM = neuralgas.SelfOrganizingMap(numberOfNeurons=self.number_of_radial, input_data_file=input,
radius=0.5, alpha=0.5)
SOM.train(50)
self.radial_layer_weights = SOM.neuron_weights
self.linear_layer_weights = 2 * np.random.random(
(self.number_of_radial + self.is_bias, self.number_of_linear)) - 1
self.delta_weights_linear_layer = np.zeros((self.number_of_radial + self.is_bias, self.number_of_linear))
self.delta_weights_radial_layer = np.zeros_like(self.radial_layer_weights)
def set_radial_coefficient(self):
for i in self.radial_layer_weights:
max = 0
for j in self.radial_layer_weights:
neural_distance = distance.euclidean(i, j)
if neural_distance > max:
max = neural_distance
self.radial_coefficient.append(max / math.sqrt(2 * self.number_of_radial))
if not self.delta_coefficient_radial_layer:
self.delta_coefficient_radial_layer = np.zeros_like(self.radial_coefficient)
def linear_func(self, x):
return x
def linear_derivative(self, x):
return 1
def rbf_gaussian(self, one_input):
output = []
for i in range(len(self.radial_layer_weights)):
output.append(np.exp(-1 * ((distance.euclidean(one_input, self.radial_layer_weights[i])) ** 2) / (
2 * self.radial_coefficient[i] ** 2)))
return output
def rbf_gaussian_derivative(self, input):
output = []
for i in range(len(input[0])):
output.append(input[:, i] * self.rbf_gaussian(i) / np.power(self.radial_coefficient, 2))
return np.asarray(output)
def rbf_gaussian_derivative_sigma(self, input):
output = []
for i in range(len(input[0])):
output.append(np.power(input[:, i], 2) * self.rbf_gaussian(i) / np.power(self.radial_coefficient, 3))
return np.asarray(output).sum(axis=0)
def feed_forward(self, input_data):
radial_layer_output = self.rbf_gaussian(input_data)
if self.is_bias == 1:
radial_layer_output = np.insert(radial_layer_output, 0, 1)
output_layer_output = self.linear_func(np.dot(radial_layer_output, self.linear_layer_weights))
return radial_layer_output, output_layer_output
def backward_propagation(self, radial_layer_output, linear_layer_output, inp, output_data):
avr_err = 0.0
output_difference = linear_layer_output - output_data
for i in output_difference:
avr_err += i ** 2
avr_err /= 2
self.epoch_error += avr_err
delta_coefficient_linear = output_difference * self.linear_derivative(linear_layer_output)
linear_adj = np.array([(radial_layer_output * delta_coefficient_linear)])
actual_linear_adj = learning_coeff * linear_adj.T + momentum_coeff * self.delta_weights_linear_layer
self.linear_layer_weights -= actual_linear_adj
self.delta_weights_linear_layer = actual_linear_adj
if self.is_derivative:
radial_layer_error = delta_coefficient_linear.dot(self.linear_layer_weights.T)
if self.is_bias:
radial_layer_error = radial_layer_error[1:]
radial_output = radial_layer_output[1:]
else:
radial_output = radial_layer_output
delta_coefficient_radial = radial_layer_error * self.rbf_gaussian_derivative(
inp - self.radial_layer_weights)
radial_adj = (radial_output * delta_coefficient_radial).T
delta_coefficient_sigma = radial_layer_error * self.rbf_gaussian_derivative_sigma(
inp - self.radial_layer_weights)
sigma_adj = radial_output * delta_coefficient_sigma
actual_radial_adj = learning_coeff * radial_adj + momentum_coeff * self.delta_weights_radial_layer
actual_radial_coefficient_adj = learning_coeff * sigma_adj \
+ momentum_coeff * self.delta_coefficient_radial_layer
self.radial_layer_weights -= actual_radial_adj
self.radial_coefficient -= actual_radial_coefficient_adj
self.delta_coefficient_radial_layer = actual_radial_coefficient_adj
self.delta_weights_radial_layer = actual_radial_adj
def train(self, epoch_count):
error_test_data_plot = []
confusion_matrix = np.zeros([self.number_of_class, self.number_of_class])
combined_data = list(zip(self.input_data, self.expected_data))
expected_amount_of_obj_in_classes = np.zeros([self.number_of_class])
assigned_amount_of_obj_in_classes_per_epoch = np.zeros([self.number_of_class])
assigned_amount_of_obj_in_classes = []
outputs = list(self.expected_data)
for i in range(len(expected_amount_of_obj_in_classes)):
expected_amount_of_obj_in_classes[i] = outputs.count(i + 1)
for epoch in range(epoch_count):
self.epoch_error = 0.0
np.random.shuffle(combined_data)
for inp, outp in combined_data:
radial_layer_output, linear_layer_output = self.feed_forward(inp)
for i in range(len(linear_layer_output)):
if int(round(linear_layer_output[i])) == outp:
assigned_amount_of_obj_in_classes_per_epoch[int(round(linear_layer_output[i] - 1))] += 1
if epoch == epoch_count - 1:
confusion_matrix[int(outp) - 1][int(round(linear_layer_output[0] - 1))] += 1
self.backward_propagation(radial_layer_output, linear_layer_output, inp, outp)
assigned_amount_of_obj_in_classes.append(assigned_amount_of_obj_in_classes_per_epoch)
assigned_amount_of_obj_in_classes_per_epoch = np.zeros([self.number_of_class])
self.epoch_error /= self.input_data.shape[0]
self.epoch_for_error.append(epoch)
self.error_for_epoch.append(self.epoch_error)
error_test_data_plot.append(self.test_network("Data/classification_test.txt", False))
print("Mean square error for last epoch: ", self.epoch_error)
print("Confusion matrix for training data:\n", confusion_matrix)
self.plot_number_of_classifications("Classification", expected_amount_of_obj_in_classes,
assigned_amount_of_obj_in_classes, "Epoch", "Number")
self.plot_uni_graph("Mean square error for testing data", np.arange(0, epoch_count, 1),
error_test_data_plot,
"Epoch",
"Error value")
self.plot_uni_graph("Mean square error for training data", self.epoch_for_error, self.error_for_epoch, "Epoch",
"Error value")
print("Error for testing data: ", self.test_network("Data/classification_test.txt", True))
def file_input(self, file_name):
with open(file_name, "r") as f:
expected_val = []
input_arr = []
data = csv.reader(f, delimiter=' ')
for row in data:
expected_val.append(int(row[-1]))
input_arr.append(np.float_(row[:-1]))
return np.asarray(input_arr), np.asarray(expected_val)
def plot_number_of_classifications(self, title, expected_matrix, actual_matrix, x_label, y_label):
colors = ['#116315', '#FFD600', '#FF6B00', '#5199ff', '#FF2970', '#B40A1B', '#E47CCD', '#782FEF', '#45D09E',
'#FEAC92']
epoch = []
for j in range(self.number_of_class):
inputX = []
for i in range(len(actual_matrix)):
inputX.append(actual_matrix[i][j])
if j == 0:
epoch.append(i)
inputX = np.asarray(inputX)
inputX = inputX / expected_matrix[j]
plt.plot(inputX, colors[j], markersize=3, marker='o', ls='', label=str(j + 1))
plt.title(title)
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.legend()
plt.show()
def plot_uni_graph(self, title, x_val, y_val, x_label, y_label):
plt.plot(x_val, y_val, 'ro', markersize=3)
plt.title(title)
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.show()
def test_network(self, test_file, is_last=False):
test_data, expected_data = self.file_input(test_file)
confusion_matrix = np.zeros([self.number_of_class, self.number_of_class])
test_output = []
err = 0.0
counter = 0
for test_pair in test_data:
hidden_layer_output_test, output_layer_output_test = self.feed_forward(test_pair)
test_output.append(output_layer_output_test)
if is_last:
confusion_matrix[int(expected_data[counter]) - 1][int(round(output_layer_output_test[0] - 1))] += 1
counter += 1
for i in range(len(test_output)):
err += (test_output[i] - expected_data[i]) ** 2
err /= 2
if is_last:
print("Confusion matrix for testing data:\n", confusion_matrix)
return err / len(test_output)