neerajvashistha

import numpy as np
import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression, RANSACRegressor


# For reproducibility
np.random.seed(1000)

nb_samples = 200

neerajvashistha

import knn_final
#do copy knn_final before running 
if __name__ == '__main__':
	from sklearn import datasets
	iris = datasets.load_iris()
	print(iris["data"])
	predictors = iris.data[:,0:2]
	outcomes = iris.target
	plt.plot(predictors[outcomes==0][:,0],predictors[outcomes==0][:,1],"ro")
	plt.plot(predictors[outcomes==1][:,0],predictors[outcomes==1][:,1],"go")

neerajvashistha

import numpy as np

def distance(p1,p2):
	""" return distance between pony p1 and p2 """
	return np.sqrt(np.sum(np.power(p2-p1,2)))

def majority_vote(votes):
	"""
	return winner for a list of votes
	or just return scipy.stats.mode(votes)

neerajvashistha

import numpy as np

def distance(p1,p2):
	""" return distance between pony p1 and p2 """
	return np.sqrt(np.sum(np.power(p2-p1,2)))

def majority_vote(votes):
	"""
	return winner for a list of votes
	or just return scipy.stats.mode(votes)

neerajvashistha

import string
alphabet = ' '+ string.ascii_lowercase
count = 0
positions=dict()
for a in alphabet:
	positions[a] = count
	count = count + 1

def shift_encode(message,key):
    encoding_list = []

neerajvashistha

absl-py==0.7.1
astor==0.8.0
atomicwrites==1.3.0
attrs==19.3.0
backcall==0.1.0
beautifulsoup4==4.8.1
bleach==3.1.0
blis==0.4.1
boto==2.49.0
boto3==1.10.0

neerajvashistha

# Common Internet File System utilities
# Wed Aug  8 12:34:05 IST 2018
sudo apt-get install cifs-utils

# Modern music player and library organiser inspired by Amarok 1.4
# Tue May 22 13:21:25 IST 2018
sudo apt-get install clementine

# cross-platform, open-source make system
# Sun Feb 18 17:55:21 IST 2018

neerajvashistha

from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier

model = LogisticRegression()
rfe = RFE(model,12)
rfe = rfe.fit(emp_train[x],emp_train[y])

print(rfe.support_)

neerajvashistha

# coding: utf-8

# In[29]:


import pandas as pd
import numpy as np

neerajvashistha

import numpy as np
import random
import time 
import matplotlib.pyplot as plt


def create_board():
	return np.zeros((3,3))

board = create_board()