nunenuh

# Looping Over Collection with Indice Forward
colors = ["red", "green", "blue"]

# bad
for i in range(len(colors)):
    print(i,'--',colors[i])
    
# good
for i, color in enumerate(colors):
    print(i, '--', color)

nunenuh

# Looping Over Two Collection
names = ["raymond", "rachel","matthew"]
colors = ["red", "green", "blue"]

# bad
n = min(len(names), len(colors))
for i in range(n):
    print(names[i], '--', colors[i])
    
# good

nunenuh

# Looping Over Collection Backward
colors = ["red", "green", "blue"]

# bad
for i in range(len(colors)-1, -1, -1):
    print(colors[i])
    
# good
for color in reversed(colors):
    print(color)

nunenuh

import sys

####################
### Set the hyperparameters in you myanswers.py file ###
####################

from my_answers import iterations, learning_rate, hidden_nodes, output_nodes

N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)

nunenuh

import numpy as np


class NeuralNetwork(object):
    def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
        # Set number of nodes in input, hidden and output layers.
        self.input_nodes = input_nodes
        self.hidden_nodes = hidden_nodes
        self.output_nodes = output_nodes

nunenuh

import random
import torch
import pathlib
import os
from torch.utils import data
import PIL
import PIL.Image

class SiameseDataset(data.Dataset):
    def __init__(self, root, ext, transform=None, pair_transform=None, target_transform=None):

nunenuh

import numpy as np
import cv2

x1, x2 = 100,400
y1, y2 = 10, 310
cap = cv2.VideoCapture(0)
while(True):
    ret, frame = cap.read()    
    r = cv2.rectangle(frame,(y1,x2),(y2,x1),(0,255,0),3)
    crop = frame[x1:x2, y1:y2]

nunenuh

import numpy as np
import cv2

def center_rectangle(frame):
    h,w,d = frame.shape
    x1,y1 = h//4, w//4
    x2,y2 = h-x1, w-y1
    rr = cv2.rectangle(frame,(y1,x1),(y2,x2),(0,255,0),3)
    crop = frame[x1:x2, y1:y2]
    return rr

nunenuh

import os
base_path = '/home/nunenuh/Desktop/skripsi/dataset/testing'

def rescale(base_path,percent):
    dirz = os.listdir(base_path)
    my_list = []

	for dr in dirz:
		path = os.path.join(base_path, dr,'*.jpg')
		my_list.append(path)

nunenuh

from pysine import sine
import numpy as np

def genfreq(num):
    tone = []
    lbfreq = [126, 135, 144, 153, 162, 171, 180,
              189, 198, 207, 216, 225, 234, 243]
    lbfreq = np.array(lbfreq)
    for i in range(0, num):
        val = lbfreq * (2**i)