mustafa-qamaruddin

function [] = mqPCA()
    global LOW_RES_INPUT_TEST_IMAGE;
    global VISUALS;
    dbl_lowres = double(LOW_RES_INPUT_TEST_IMAGE);
    width = size(LOW_RES_INPUT_TEST_IMAGE,1);
    height = size(LOW_RES_INPUT_TEST_IMAGE,2);

    rshp_lowres = reshape(LOW_RES_INPUT_TEST_IMAGE, width*height, 3);

    %% figure, imshow(rshp_lowres);

mustafa-qamaruddin

#include <iostream>
#include <fstream>
#include <string>

using namespace std;

typedef unsigned long long ull;

const int MAX_SIZE = 100000;
int data[MAX_SIZE];

mustafa-qamaruddin

// pass by value
var y = 10;

function modify(x)
{
  x = 5;
}

modify(y);

mustafa-qamaruddin

// pass by reference
var arr = [1,2,3];

function append(_arr)
{
  _arr.push(90);
}

append(arr);

mustafa-qamaruddin

// pass by value ; date
var d = new Date('July 13, 2016');

console.log(d.getTime());
function mutate(_d)
{
  _d = new Date('August 20, 1989');
  console.log(_d.getTime());
}

mustafa-qamaruddin

import matplotlib.pyplot as plt
import numpy as np


ls_names = ["Omar", "Zainuddin", "Yahya", "Malek"]

scores = {"Omar": 0, "Zainuddin": 0, "Yahya": 0, "Malek": 0}

for j in range(10):
    print("{}/10".format(j), end="\r")

mustafa-qamaruddin

$ wget http://www.cryptodatadownload.com/cdd/Gemini_ETHUSD_d.csv

mustafa-qamaruddin

df = pd.read_csv('./Gemini_ETHUSD_d.csv', skiprows=1)
for i in range(1, STEPS):
    col_name = 'd{}'.format(i)
    df[col_name] = df['d0'].shift(periods=-1 * i)
df = df.dropna()

mustafa-qamaruddin

X = df.iloc[:, :TRAIN_STEPS]
y = df.iloc[:, TRAIN_STEPS:]

X_train = X.iloc[:SPLIT_IDX, :]
y_train = y.iloc[:SPLIT_IDX, :]

X_test = X.iloc[SPLIT_IDX:, :]
y_test = y.iloc[SPLIT_IDX:, :]

mustafa-qamaruddin

def build_model(_alpha, _l1_ratio):
    estimator = ElasticNet(
        alpha=_alpha,
        l1_ratio=_l1_ratio,
        fit_intercept=True,
        normalize=False,
        precompute=False,
        max_iter=16,
        copy_X=True,
        tol=0.1,