ankitmishra88

import numpy as np
import matplotlib.pyplot as plt
def plot(x,y,m,b):
    plt.scatter(x,y)
    # datapoints
    y_pred=[i*m+b for i in x]
    plt.plot(x,y) #classifier line
    plt.show()
    

ankitmishra88

import numpy as np
def perceptron(features,labels):
    theta=np.array([0]*len(features[0]))
    theta0=0
    t=10
    while(t):
        t=t-1
        for i in range(len(features)):
            if (np.dot(features[i],theta)+theta0)*labels[i]<=0:
                #print('mistake')

ankitmishra88

import numpy as np
def perceptron(features,labels):
    theta=np.array([0]*len(features[0]))
    theta0=0
    t=10
    count=1
    sum_theta=theta
    sum_theta_0=theta0
    while(t):
        t=t-1

ankitmishra88

import numpy as np
import math
import matplotlib.pyplot as plt
#----------Pegasos Code definition------------------------#
def pegasos(feature_matrix,labels,T):
  theta=np.array([0]*feature_matrix.shape[1])
  theta_0=0
  count=0
  L=2 #value for lambda
  for i in range(T):

ankitmishra88

ankitmishra88

#-------pi(r^2)=(n!)^2
import math
fact=[1]
#----------Preprocessing---------------#
def preprocess():
    for i in range(100000):
        fact.append((fact[i]*(i+1))%1000000007)
def main():
        preprocess()
        t=int(input())

ankitmishra88

p_arr=[]
arr=[[0,True,0] for i in range(1000001)]
def preprocess():
    arr[0][1]=False
    arr[1][1]=False
    arr[2][2]=2
    p=2
    
    while(p*p<=1000001):
        #print(p,arr[p][1])

ankitmishra88