anujonthemove

Emoji	Purpose	MD Markup	Prefix
📄	Generic message	:page_facing_up:
📐	Improve the format / structure of the code / files	:triangular_ruler:	[IMPROVE]:
⚡	Improve performance	:zap:	[IMPROVE]:
🚀	Improve something (anything)	:rocket:	[IMPROVE]:
📝	Write docs	:memo:	[PROD]:
💡	New idea

anujonthemove

def get_classification_report(y_test, y_pred):
    '''Source: https://stackoverflow.com/questions/39662398/scikit-learn-output-metrics-classification-report-into-csv-tab-delimited-format'''
    from sklearn import metrics
    report = metrics.classification_report(y_test, y_pred, output_dict=True)
    df_classification_report = pd.DataFrame(report).transpose()
    df_classification_report = df_classification_report.sort_values(by=['f1-score'], ascending=False)
    return df_classification_report

anujonthemove

import numpy as np
import cv2


class BackGroundSubtractor:
	# When constructing background subtractor, we
	# take in two arguments:
	# 1) alpha: The background learning factor, its value should
	# be between 0 and 1. The higher the value, the more quickly
	# your program learns the changes in the background. Therefore, 

anujonthemove

anujonthemove

import tensorflow as tf

x = tf.Variable(2, name='x', dtype=tf.float32)
log_x = tf.log(x)
log_x_squared = tf.square(log_x)

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(log_x_squared)

init = tf.initialize_all_variables()

anujonthemove

// takes the form field value and returns true on valid number
function valid_credit_card(value) {
  // accept only digits, dashes or spaces
	if (/[^0-9-\s]+/.test(value)) return false;

	// The Luhn Algorithm. It's so pretty.
	var nCheck = 0, nDigit = 0, bEven = false;
	value = value.replace(/\D/g, "");

	for (var n = value.length - 1; n >= 0; n--) {

anujonthemove

namespace cv {
    // calculates the median value of a single channel
    // based on https://github.com/arnaudgelas/OpenCVExamples/blob/master/cvMat/Statistics/Median/Median.cpp
    double median( cv::Mat channel )
    {
        double m = (channel.rows*channel.cols) / 2;
        int bin = 0;
        double med = -1.0;

        int histSize = 256;