Create gaussian_discriminant_analysis.py

generative_models/gaussian_discriminant_analysis.py

@@ -0,0 +1,45 @@
+import numpy as np
+import pandas as pd
+
+
+class GaussianDiscriminantAnalysis(): ## requires data as a pandas dataframe in the format [atribute, atribute, ....., class]
+ def __init__(self, df):
+ self.data = df
+ self.N = self.data.shape[0]
+ self.classes = list(set(self.data.iloc[:, -1]))
+ self.splits = []
+ self.means = []
+ self.covariances = []
+ for label in self.classes:
+ split = self.data.loc[self.data.iloc[:, -1] == label].iloc[:, 0:-1].astype(float)
+ self.splits.append(split)
+ self.means.append((np.mean(np.asarray(split),axis=0)))
+ self.covariances.append(np.cov(np.asarray(split), rowvar=False))
+
+ def gaussian_Probability(self, x, mean, covariance):
+ n = np.shape(x)[0]
+ nominator = np.exp((-0.5) * (np.dot(np.transpose(x - mean), np.matmul(np.linalg.inv(covariance), (x - mean)))))
+ denominator = (((2.0 * np.pi) ** (n / 2.0)) * np.sqrt(np.linalg.norm(covariance)))
+ return np.divide(nominator, denominator)
+
+ def classify(self, datapoint):
+ # print("-------------------\n")
+ # print(datapoint)
+ classification = {}
+ p_of_x_given_y = []
+ P_of_Y = []
+ P_of_Y_given_X = []
+ for index, label in enumerate(self.classes):
+ split = self.data.loc[self.data.iloc[:, -1] == label].iloc[:, 0:-1]
+ # print(index,label)
+ P_of_Y.append(len(split) / (self.N))
+ p_of_x_given_y.append(self.gaussian_Probability(datapoint, self.means[index], self.covariances[index]))
+ P_of_X = sum([p_of_x_given_y[i] * P_of_Y[i] for i in range(len(self.classes))])
+ # print(P_of_Y,"P(Y)")
+ # print(p_of_x_given_y,"P(X|Y)")
+ # print(P_of_X, "P(X)")#
+ for index, label in enumerate(self.classes):
+ P_of_Y_given_X.append((p_of_x_given_y[index] * P_of_Y[index]) / P_of_X)
+ classification.update({str(label): (p_of_x_given_y[index] * P_of_Y[index]) / P_of_X})
+
+ return classification