mronian

{
	"Version": "2012-10-17",
	"Statement": [
		{
			"Sid": "VisualEditor0",
			"Effect": "Allow",
			"Action": [
				"organizations:ListHandshakesForOrganization",
				"organizations:InviteAccountToOrganization",
				"organizations:CancelHandshake",

mronian

#include <stdio.h>

int main()
{
	int n, i, num_zeros = 0, num_neg = 0, num_pos = 0, a[100];
	printf("Please enter value of N:");
	scanf("%d", &n);

	if(n<=0)
	{

mronian

#include <stdio.h>
#include <limits.h> // Used for INT_MIN

int main()
{
	int n, i, max = INT_MIN, a[100];
	double avg = 0.0;
	printf("Please enter value of N:");
	scanf("%d", &n);

mronian

data = importdata('breastCancer.csv',',');

[n,m] = size(data);

epsilon = 1e-1;

%x(1:n,1:m-1) = 0;
y(1:n,1:1) = 0;
a = 0.1;
%t(1:n,1:1) = 0;

mronian

data = importdata('breastCancer.csv',',');

[n,m] = size(data);

x(1:n,1:m-1) = 0;
y(1:n,1:1) = 0;
t(1:n,1:1) = 0;
w_old = zeros(1,m-1);

t(:,1) = data(:,1);

mronian

import csv
import random
import math
import numpy as np
from sklearn import cross_validation
from sklearn.cross_validation import train_test_split
from sklearn.metrics import mean_squared_error, accuracy_score

class NaiveBayes:
	

mronian

import numpy as np
data=np.random.normal(5, 2**0.5, 1000)

split_data=np.split(data,10)

means=[]
variances=[]

for sd in split_data:
	means.append(np.mean(sd))

mronian

import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, accuracy_score
import math

gammas=[0.001, 0.01, 0.1, 1, 10, 100, 1000]

avalues=[0.1, 0.5, 1, 2, 10]

mronian

import numpy as np
from sklearn.cross_validation import train_test_split
from sklearn import linear_model
from sklearn.metrics import mean_squared_error, accuracy_score
data=np.genfromtxt('housing.txt')
b=data[:,0]
a=data[:,1:]

a_train, a_test, b_train, b_test = train_test_split(a, b, test_size=0.40, random_state=42)

mronian

x=imread('Picture.jpg');
y=rgb2gray(x) ;
z=histeq(y);
t=im2bw(z);
u=double(t);
[a b]=size(u);
for i=1:a
    c=1;
for j=1:b
   if u(i,j)==1