TommyHuang821

import numpy as np
n_bit = 4
xf= np.array([0.1,0.2,1.2,3,2.1,-2.1,-3.5])
xf= np.array([15,25,35,40,50, 60, 70])

range_xf=np.max(xf)-np.min(xf)
print('range:{}'.format(range_xf))
alpha = (2**(n_bit-0)-1)/(range_xf)
print('alpha:{}'.format(alpha))
zp=np.round(np.min(xf)*alpha)

TommyHuang821

import numpy as np
n_bit = 4
xf= np.array([0.1,0.2,1.2,3,2.1,-2.1,-3.5])
xf= np.array([15,25,35,40,50, 60, 70])
range_xf=np.max(np.abs(xf))
print('range:{}'.format(range_xf))
alpha = (2**(n_bit-1)-1)/(range_xf)
print('alpha:{}'.format(alpha))
xq=np.round(alpha*xf)
print('xq:{}'.format(xq))

TommyHuang821

# -*- coding: utf-8 -*-
"""
@author: Chih-Sheng Huang (Tommy), chih.sheng.huang821@gmail.com
"""

import torch
import torch.nn as nn
import torch.utils.data as Data
import matplotlib.pyplot as plt
import numpy as np

TommyHuang821

# -*- coding: utf-8 -*-
"""
Created on Mon Nov 26 21:38:26 2018
reference : https://github.com/conordewey3/Medium-Stats-Analysis/blob/master/Medium%20Stats%20Data%20Collection.ipynb
@author: Tommy
"""
# Imports
from selenium import webdriver
import time
from bs4 import BeautifulSoup

TommyHuang821

# -*- coding: utf-8 -*-
"""
Created on Thu May 31 11:21:44 2018
Machine learning Example for「SMS Spam Collection Dataset」
Database Link:
https://archive.ics.uci.edu/ml/datasets/sms+spam+collection
https://www.kaggle.com/uciml/sms-spam-collection-dataset#spam.csv
 
@author: Tommy Huang, chih.sheng.huang821@gmail.com
"""