sajith-rahim

package com.examples.util;

import java.io.File;
import java.io.FileInputStream;
import java.io.FileOutputStream;
import java.io.IOException;
import java.util.Properties;

public class PropertiesReadWrite {

sajith-rahim

Add custom domain to your github page.

1. Log in to your domain registrar and find where to change your host records.
2. Change your domain's Address Record to 204.232.175.78. This is GitHub's IP address, which allows GitHub to resolve your URL and serve the correct files.
3. In your website's directory folder on your computer, create a file called "CNAME". On the first line, type your domain name. Save the file.
4. In your GitHub application, you should see the file in the left column. Make sure it is checked and enter your commit message.
5. Click "Sync branches."
6. It can take as long as 48 hours for your domain to resolve to your GitHub page. However, it is usually pretty quick, so check back in an hour or so.

sajith-rahim

import datetime
import logging
import ntpath
import os
from typing import Optional


def create_folder(directory):
    if not os.path.exists(directory):
        os.makedirs(directory)

sajith-rahim

    u = [0, 0, 1, 6]
    v = [0, 0, 4, 2]
    u_bis = [1, 6, v[2], v[3]]
    w = [0, 0, 5, 8]
    plt.quiver([u[0], u_bis[0], w[0]],
               [u[1], u_bis[1], w[1]],
               [u[2], u_bis[2], w[2]],
               [u[3], u_bis[3], w[3]],
               angles='xy', scale_units='xy', scale=1, color=sns.color_palette())
    plt.xlim(-2, 6)

sajith-rahim

def cosine_sim(vec1, vec2):
    if isinstance(vec1, list) and isinstance(vec2, list):
        dot_prod = 0
        for i, v in enumerate(vec1):
            dot_prod += v * vec2[i]
        mag_1 = math.sqrt(sum([x ** 2 for x in vec1]))
        mag_2 = math.sqrt(sum([x ** 2 for x in vec2]))
        return dot_prod / (mag_1 * mag_2)

sajith-rahim

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.metrics import mean_squared_error


class LinearRegression:
    """
    Linear Regression
    Parameters:

sajith-rahim

import numpy as np;


def gradient_descent(x, y):
    m = 0.0
    b = 0.0
    iterations = 10000
    learning_rate = 1e-4

    x_mean = np.mean(x)