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'''Name: Ritam Chakraborty
   Roll: B20127
   Phone: 7439257709'''

# import libraries
import pandas as pd
import numpy as np
from numpy.linalg import det, inv
# for computing confusion matrix and accuracy score
from sklearn.metrics import confusion_matrix, accuracy_score
# for making comparator to sort the distances and corresponding classes based on distances

def prob(x, cov_mat, mu_mat, Pci):
    diff = x-mu_mat
    diff_T = diff.T
    inv_cov_mat = inv(cov_mat)
    dotprod = np.dot(diff.T, inv_cov_mat)
    prod = np.dot(dotprod, diff)
    return Pci*np.exp(-0.5*prod)/(((2*np.pi)**(12.5))*np.sqrt(det(cov_mat)))

df_train = pd.read_csv("SteelPlateFaults-train.csv")
df_test = pd.read_csv("SteelPlateFaults-test.csv")
# remove the following attributes since they make the covariance matrix singular
df_train.__delitem__("TypeOfSteel_A300")
df_train.__delitem__("TypeOfSteel_A400")
df_test.__delitem__("TypeOfSteel_A300")
df_test.__delitem__("TypeOfSteel_A400")

# separate the classes from the training dataset
df_class1 = df_train[df_train["Class"] == 1].copy()
df_class1.__delitem__("Class")
df_class0 = df_train[df_train["Class"] == 0].copy()
df_class0.__delitem__("Class")

# compute mean vectors from both classes
mu1 = df_class1.mean().to_numpy()
mu0 = df_class0.mean().to_numpy()

# predict based on bayes classifier
pred = []
for ind in df_test.index:
    p1 = prob(df_test.iloc[ind, :25].to_numpy(), df_class1.cov(), mu1, 509/(509+273))
    p0 = prob(df_test.iloc[ind, :25].to_numpy(), df_class0.cov(), mu0, 273/(509+273))
    if p1 > p0:
        pred.append(1)
    else:
        pred.append(0)

#print the confusion matrix and accuracy scores
print("Confusion matrix:")
print(confusion_matrix(df_test["Class"], pred))
print("Accuracy score:")
print(accuracy_score(df_test["Class"], pred))