Ankit Mishra ankitmishra88
ankitmishra88
/ pandasTutorial.ipynb
Last active
June 9, 2020 03:04
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ankitmishra88
/ prime_distance.py
Created
April 6, 2020 08:50
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| 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
/ brilliant-mind.py
Last active
April 5, 2020 10:18
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| #-------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
/ Support-vector-machine-implementation.ipynb
Created
March 29, 2020 19:35
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ankitmishra88
/ pegasos(SVM).py
Created
March 26, 2020 06:37
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| 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
/ averaged_perceptron.py
Last active
March 25, 2020 04:22
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| 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
/ Petceptron_Algorithm.py
Created
March 24, 2020 21:38
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| 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
/ gradient_descent.py
Last active
March 26, 2020 16:05
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| 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() | |