Henry Kim Kcrong

## auto_comp.py
ai = input('num 1: ')
bi = input('num 2: ')
ci = input('num 3: ')

inp = sorted([ai, bi, ci])
operators = ['>', '==', '<']


def exp_c(a, b, op):
    return eval("{} {} {}".format(a, op, b))

## little_strokes_fell_great_oaks.py
# with for loop
# range() --> starts from 0

def with_for_loop():
  for i in range(10):
    print('Hit %d times' % (i+1))
  print('The tree fell down!!')

def with_while_loop():
  hit = 0

## func.py
def seq2dataset(seq, window_size):
    dataset = []
    for i in range(len(seq)-window_size):
        subset = seq[i:(i+window_size+1)]
        dataset.append([code2idx[item] for item in subset])
    return np.array(dataset)

## scale
code2idx = {'c4':0, 'd4':1, 'e4':2, 'f4':3, 'g4':4, 'a4':5, 'b4':6,
            'c8':7, 'd8':8, 'e8':9, 'f8':10, 'g8':11, 'a8':12, 'b8':13}

idx2code = {0:'c4', 1:'d4', 2:'e4', 3:'f4', 4:'g4', 5:'a4', 6:'b4',
            7:'c8', 8:'d8', 9:'e8', 10:'f8', 11:'g8', 12:'a8', 13:'b8'}

# Or use dict comprehension
idx2code = {v: k for k, v in code2idx.items()}

## seq.py
# Whole data
seq = ['g8', 'e8', 'e4', 'f8', 'd8', 'd4', 'c8', 'd8', 'e8', 'f8', 'g8', 'g8', 'g4',
       'g8', 'e8', 'e8', 'e8', 'f8', 'd8', 'd4', 'c8', 'e8', 'g8', 'g8', 'e8', 'e8', 'e4',
       'd8', 'd8', 'd8', 'd8', 'd8', 'e8', 'f4', 'e8', 'e8', 'e8', 'e8', 'e8', 'f8', 'g4',
       'g8', 'e8', 'e4', 'f8', 'd8', 'd4', 'c8', 'e8', 'g8', 'g8', 'e8', 'e8', 'e4']

## data url
http://tykimos.github.io/warehouse/2017-3-8_CNN_Getting_Started_handwriting_shape.zip

## data.csv

          
            6
            148
            72
            35
            0
            33.6
            0.627
            50
            1

            
              1
              85
              66
              29
              0
              26.6
              0.351
              31
              0

            
              8
              183
              64
              0
              0
              23.3
              0.672
              32
              1

            
              1
              89
              66
              23
              94
              28.1
              0.167
              21
              0

            
              0
              137
              40
              35
              168
              43.1
              2.288
              33
              1

            
              5
              116
              74
              0
              0
              25.6
              0.201
              30
              0

            
              3
              78
              50
              32
              88
              31.0
              0.248
              26
              1

            
              10
              115
              0
              0
              0
              35.3
              0.134
              29
              0

            
              2
              197
              70
              45
              543
              30.5
              0.158
              53
              1

            
              8
              125
              96
              0
              0
              0.0
              0.232
              54
              1

## first_sample.py
from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], 784).astype('float32') / 255.0
x_test = x_test.reshape(x_test.shape[0], 784).astype('float32') / 255.0
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)

## async_await_help.py


"""
1. main 함수에서 `async def A`를 호출 후 await 하고, 반환값을 받아 다시 `async def B`를 호출하는 방식

"""


import asyncio
from socket import socket, AF_INET, SOCK_STREAM

## script.py
import json
import pandas as pd

from collections import defaultdict

multi_depth_defaultdict = lambda: defaultdict(multi_depth_defaultdict)

df = pd.read_excel('category.xlsx')

data = multi_depth_defaultdict()
	ai = input('num 1: ')
	bi = input('num 2: ')
	ci = input('num 3: ')

	inp = sorted([ai, bi, ci])
	operators = ['>', '==', '<']


	def exp_c(a, b, op):
	return eval("{} {} {}".format(a, op, b))
	# with for loop
	# range() --> starts from 0

	def with_for_loop():
	for i in range(10):
	print('Hit %d times' % (i+1))
	print('The tree fell down!!')

	def with_while_loop():
	hit = 0
	def seq2dataset(seq, window_size):
	dataset = []
	for i in range(len(seq)-window_size):
	subset = seq[i:(i+window_size+1)]
	dataset.append([code2idx[item] for item in subset])
	return np.array(dataset)
	code2idx = {'c4':0, 'd4':1, 'e4':2, 'f4':3, 'g4':4, 'a4':5, 'b4':6,
	'c8':7, 'd8':8, 'e8':9, 'f8':10, 'g8':11, 'a8':12, 'b8':13}

	idx2code = {0:'c4', 1:'d4', 2:'e4', 3:'f4', 4:'g4', 5:'a4', 6:'b4',
	7:'c8', 8:'d8', 9:'e8', 10:'f8', 11:'g8', 12:'a8', 13:'b8'}

	# Or use dict comprehension
	idx2code = {v: k for k, v in code2idx.items()}
	# Whole data
	seq = ['g8', 'e8', 'e4', 'f8', 'd8', 'd4', 'c8', 'd8', 'e8', 'f8', 'g8', 'g8', 'g4',
	'g8', 'e8', 'e8', 'e8', 'f8', 'd8', 'd4', 'c8', 'e8', 'g8', 'g8', 'e8', 'e8', 'e4',
	'd8', 'd8', 'd8', 'd8', 'd8', 'e8', 'f4', 'e8', 'e8', 'e8', 'e8', 'e8', 'f8', 'g4',
	'g8', 'e8', 'e4', 'f8', 'd8', 'd4', 'c8', 'e8', 'g8', 'g8', 'e8', 'e8', 'e4']
6	148	72	35	0	33.6	0.627	50	1
1	85	66	29	0	26.6	0.351	31	0
8	183	64	0	0	23.3	0.672	32	1
1	89	66	23	94	28.1	0.167	21	0
0	137	40	35	168	43.1	2.288	33	1
5	116	74	0	0	25.6	0.201	30	0
3	78	50	32	88	31.0	0.248	26	1
10	115	0	0	0	35.3	0.134	29	0
2	197	70	45	543	30.5	0.158	53	1
8	125	96	0	0	0.0	0.232	54	1
	from keras.utils import np_utils
	from keras.datasets import mnist
	from keras.models import Sequential
	from keras.layers import Dense, Activation

	(x_train, y_train), (x_test, y_test) = mnist.load_data()
	x_train = x_train.reshape(x_train.shape[0], 784).astype('float32') / 255.0
	x_test = x_test.reshape(x_test.shape[0], 784).astype('float32') / 255.0
	y_train = np_utils.to_categorical(y_train)
	y_test = np_utils.to_categorical(y_test)


	"""
	1. main 함수에서 `async def A`를 호출 후 await 하고, 반환값을 받아 다시 `async def B`를 호출하는 방식

	"""


	import asyncio
	from socket import socket, AF_INET, SOCK_STREAM
	import json
	import pandas as pd

	from collections import defaultdict

	multi_depth_defaultdict = lambda: defaultdict(multi_depth_defaultdict)

	df = pd.read_excel('category.xlsx')

	data = multi_depth_defaultdict()