yujuwon

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

x_label0 = np.random.normal(5,1,10)
x_label1 = np.random.normal(2,1,10)
xs = np.append(x_label0, x_label1)
labels = [0.] * len(x_label0) + [1.] * len(x_label1)

yujuwon

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

learning_rate = 0.001
training_epochs = 1000
reg_lambda = 0.
x_dataset = np.linspace(-1, 1, 100)
num_coeffs = 9

yujuwon

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os.path
import re
import sys
import numpy as np
import tensorflow as tf

yujuwon

    private void setContent(){
        String tag = "";
        int i;
        for(i = 0 ; i <mTagLists.size(); i++){
            tag += "#" + mTagLists.get(i) + " ";
        }

        ArrayList<int[]> hashtagSpans = getSpans(tag, '#');

        SpannableString tagsContent = new SpannableString(tag);

yujuwon

public class Hashtag extends ClickableSpan {
  
    public interface ClickEventListener{
        void onClickEvent(String data);
    }

    private ClickEventListener mClickEventListener = null;
    private Context context;
    private TextPaint textPaint;

yujuwon

public class CircleTransform implements Transformation {
    @Override
    public Bitmap transform(Bitmap source) {
        int size = Math.min(source.getWidth(), source.getHeight());

        int x = (source.getWidth() - size) / 2;
        int y = (source.getHeight() - size) / 2;

        Bitmap squaredBitmap = Bitmap.createBitmap(source, x, y, size, size);

yujuwon

private CallbackManager mCallbackManager;
private AccessToken mToken = null;

@Override
protected void onCreate(Bundle savedInstanceState){
  super.onCreate(savedInstanceState);
  FacebokSdk.sdkInitialize(getApplicationContext());
  mCallbackManager = CallbackManager.Factory.create();
  mToken = AccessToken.getCurrentAccessToken();
  

yujuwon

import os,sys
import Image

size = 256, 256

for (path, dirname,files) in os.walk(sys.argv[1]):
  for f in files:
    ext = os.path.splitext(f)
    upper_ext = ext[1].upper()
    outfile = os.path.join(path, '/root/output', ext[0] + '_256x256' + upper_ext)

yujuwon

import tensorflow as tf
import sys
sys.path.append("/root/work/deep/code/01_mnist_beginning")
import input_data
import numpy
import math
import time

NUM_CLASSES = 10
IMAGE_SIZE = 28

yujuwon

def createDataSet():
  dataSet = [[1,1,'yes'], [1,1,'yes'], [1,0,'no'], [0,1,'no'], [0,1,'no']]
  labels = ['no surfacing', 'flippers']
  return dataSet, labels
  
def majorityCnt(classList):
  classCount={}
  for vote in classList:
    if vote not in classCount.keys():
      classCount[vote] = 0