yusugomori

###!
 * jFBMobileFeedPhoto.js
 *
 * @version  1.0.2
 * @author   Yusuke Sugomori
 * @license  http://yusugomori.com/license/mit The MIT License
 *
 * More details on github: https://github.com/yusugomori/jFBMobileFeedPhoto
 ###

yusugomori

class Landscape
  constructor: (options={}) ->
    $('body').css visibility: 'hidden'

    @isPortraitActivated = false
    @isLandscape = false
    @winWidth = 0
    @clientHeight = 0
    @clientWidth = 0
    @maxWidth = 0

yusugomori

#Landscape
  #wrapper
    .view-host
      //.view.full-image
        = image_tag "/path/to/img"
      .view.page
        p page
      .view.page
        p page
       

yusugomori

@import "bootstrap/bootstrap";

@ratio: 1.1985;
@w: 480px;

body {
  min-height: 100%;
  overflow: hidden;
}

yusugomori

class iPhoneTouchScroll
  constructor: () ->
    @clientHeight = 0
    @preserveHeight = 0
    @onTouchCurrent = {x:0, y:0}
    @onTouchStart = {x:0, y:0}
    @onTouchDrag = {x:0, y:0}
    @onTouchDragThres = 5
    # @onTouchScrollThres = 0
    @onTouchScrollThres = 50

yusugomori

String.prototype.capitalize = function() {
  return this.slice(0, 1).toUpperCase() + this.slice(1).toLowerCase();
};

console.log('hElLo'.capitalize());  // Hello

yusugomori

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""
 Restricted Boltzmann Machine (RBM)

 References :
   - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise
   Training of Deep Networks, Advances in Neural Information Processing
   Systems 19, 2007

yusugomori

#!/usr/bin/env python
# -*- coding: utf-8 -*-

'''
 Logistic Regression
 
 References :
    - Jason Rennie: Logistic Regression,
   http://qwone.com/~jason/writing/lr.pdf
 

yusugomori

#!/usr/bin/env python
# -*- coding: utf-8 -*-

'''
 Deep Belief Nets (DBN)

 References :
   - Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle: Greedy Layer-Wise
   Training of Deep Networks, Advances in Neural Information Processing
   Systems 19, 2007

yusugomori

# Mathieu Blondel, September 2010

import numpy as np
from numpy import linalg
import cvxopt
import cvxopt.solvers

def linear_kernel(x1, x2):
    return np.dot(x1, x2)