C2_x.append(float(rowItem))
firstItemAppended = True
elif( firstItemAppended == True):
C2_y.append(float(rowItem))
C1_features = list()
C2_features = list()
C1_z = np.array(C1_x)**2 + np.array(C1_y)**2
C2_z = np.array(C2_x)**2 + np.array(C2_y)**2
C1_features.append(C1_x)
C1_features.append(C1_y)
#C1_features.append(C1_z)
C2_features.append(C2_x)
C2_features.append(C2_y)
#C2_features.append(C2_z)
classifier = BayesClassifier.GaussianBayesClassifier()
C1_distribution = classifier.getClassDistribution(C1_features)
C2_distribution = classifier.getClassDistribution(C2_features)
C1_x_variance = C1_distribution[1][0] * C1_distribution[1][0]
C1_y_variance = C1_distribution[1][1] * C1_distribution[1][1]
C1_cov_matrix = classifier.calc_2d_covariance_matrix(C1_x, C1_y,
C1_distribution[0][0],C1_distribution[0][1],C1_x_variance,C1_y_variance)
C2_x_variance = C2_distribution[1][0] * C2_distribution[1][0]
C2_y_variance = C2_distribution[1][1] * C2_distribution[1][1]
C2_cov_matrix = classifier.calc_2d_covariance_matrix(C2_x, C2_y,
C2_distribution[0][0],C2_distribution[0][1],C2_x_variance,C2_y_variance)
x_est50 = list(np.arange(-6, 6, 0.1))
y_est50 = []
