i want this python code to be converted into c# code.

import csv import numpy as np from sklearn.metrics import confusion_matrix, f1_score, roc_curve, auc import matplotlib.pyplot as plt from itertools import cycle from scipy import interp import warnings import random import math

#convert txt file to csv with open('heartdisease.txt', 'r') as in_file: stripped = (line.strip() for line in in_file) lines = (line.split(",") for line in stripped if line) with open('heartdisease.csv', 'w',newline='') as out_file: writer = csv.writer(out_file) writer.writerow(('age', 'sex', 'cp', 'restbp', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca','thal', 'num')) writer.writerows(lines) warnings.filterwarnings("ignore") # Example of Naive Bayes implemented from Scratch in Python

#calculating mean of column values belonging to one class def mean(columnvalues): s=0 n=float(len(columnvalues)) for i in range(len(columnvalues)): s=s+float(columnvalues[i]) return s/n

#calculating standard deviation of column values belonging to one class def stdev(columnvalues): avg = mean(columnvalues) s=0.0 num=len(columnvalues) for i in range(num): s=s+pow(float(columnvalues[i])-avg,2) variance = s/(float(num-1)) return math.sqrt(variance)

# Reading CSV file filename = 'heartdisease.csv' lines = csv.reader(open(filename, "r")) dataset = list(lines) for i in range(len(dataset)-1): dataset[i] = [float(x) for x in dataset[i+1]]

for z in range(5): print(" Test Train Split no. ",z+1," ") trainsize = int(len(dataset) * 0.75) trainset = [] testset = list(dataset) for i in range(trainsize): index = random.randrange(len(testset)) trainset.append(testset.pop(index))

# separate list according to class classlist = {} for i in range(len(dataset)): class_num = float(dataset[i][-1]) row=dataset[i] if (class_num not in classlist): classlist[class_num] = [] classlist[class_num].append(row) # preparing data class wise class_data = {} for class_num, row in classlist.items(): class_datarow = [(mean(columnvalues), stdev(columnvalues)) for columnvalues in zip(*row)] class_datrow=class_datarow[0:13] class_data[class_num] =class_datarow # Getting test vector y_test=[] for j in range(len(testset)): y_test.append(testset[j][-1]) # Getting prediction vector y_pred = [] for i in range(len(testset)): class_probability = {} for class_num, row in class_data.items(): class_probability[class_num] = 1 for j in range(len(row)): calculated_mean, calculated_dev = row[j] x = float(testset[i][j]) if(calculated_dev!=0): power =math.exp(-(math.pow(x-calculated_mean,2)/(2*math.pow(calculated_dev,2)))) probability= (1 / (math.sqrt(2*math.pi) *calculated_dev)) * power class_probability[class_num] *= probability

resultant_class, max_prob = -1, -1 for class_num, probability in class_probability.items(): if resultant_class == -1 or probability > max_prob: max_prob = probability resultant_class = class_num y_pred.append(resultant_class) # Getting Accuracy count = 0 for i in range(len(testset)): if testset[i][-1] == y_pred[i]: count += 1 accuracy=(count/float(len(testset))) * 100.0 print(" Accuracy: ",accuracy,"%")

y1=[float(k) for k in y_test] y_pred1=[float(k) for k in y_pred] print(" Confusion Matrix") cf_matrix=confusion_matrix(y1,y_pred1) print(cf_matrix) print(" F1 Score") f_score = f1_score(y1,y_pred1,average='weighted') print(f_score) # Matrix from 1D array y2=np.zeros(shape=(len(y1),5)) y3=np.zeros(shape=(len(y_pred1),5)) for i in range(len(y1)): y2[i][int(y1[i])]=1 for i in range(len(y_pred1)): y3[i][int(y_pred1[i])]=1 # ROC Curve generation n_classes = 5 fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y2[:, i], y3[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) # Compute micro-average ROC curve and ROC area fpr["micro"], tpr["micro"], _ = roc_curve(y2.ravel(), y3.ravel()) roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) # Compute macro-average ROC curve and ROC area print(" ROC Curve") # First aggregate all false positive rates lw=2 all_fpr = np.unique(np.concatenate([fpr[i] for i in range(n_classes)])) # Then interpolate all ROC curves at this points mean_tpr = np.zeros_like(all_fpr) for i in range(n_classes): mean_tpr += interp(all_fpr, fpr[i], tpr[i]) # Finally average it and compute AUC mean_tpr /= n_classes fpr["macro"] = all_fpr tpr["macro"] = mean_tpr roc_auc["macro"] = auc(fpr["macro"], tpr["macro"]) # Plot all ROC curves plt.figure() plt.plot(fpr["micro"], tpr["micro"], label='micro-average (area = {0:0.2f})' ''.format(roc_auc["micro"]), color='deeppink', linestyle=':', linewidth=4) plt.plot(fpr["macro"], tpr["macro"], label='macro-average (area = {0:0.2f})' ''.format(roc_auc["macro"]), color='navy', linestyle=':', linewidth=4) colors = cycle(['aqua', 'darkorange', 'cornflowerblue','red','black']) for i, color in zip(range(n_classes), colors): plt.plot(fpr[i], tpr[i], color=color, lw=lw, label='ROC of class {0} (area = {1:0.2f})' ''.format(i, roc_auc[i])) plt.plot([0, 1], [0, 1], 'k--', lw=lw) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel('False Positive Rate') plt.ylabel('True Positive Rate') plt.title('Receiver operating characteristic for multi-class') plt.legend(loc="lower right") plt.show()