Skip to content

Instantly share code, notes, and snippets.

View ytakashina's full-sized avatar
🌊

Yuya Takashina ytakashina

🌊
25 followers · 3 following
View GitHub Profile
@ytakashina
ytakashina / jupyter-import.md
Last active November 16, 2017 07:27
Jupyter notebook の import 用 snippets 。
  • data
import tqdm
import pickle
import numpy as np
import scipy
import sklearn
import pandas as pd
import plotly
@ytakashina
ytakashina / setup.sh
Last active September 25, 2017 12:30
if [ "$(id -u)" != "0" ]; then
echo "Installation was failed. Run as a super user!" 1>&2
return 1
fi
apt update && apt -y upgrade
apt install -y git vim w3m wget tmux vsftpd graphviz openssh-server build-essential
apt upgrade gcc cmake
@ytakashina
ytakashina / pip
Last active August 17, 2017 01:25
pip install tqdm numpy cupy scipy scikit-learn pandas seaborn bokeh graphviz pystan chainer tensorflow keras
@ytakashina
ytakashina / plotly-network.py
Created August 25, 2017 11:12
plotly でネットワークを描画しようとした
import plotly
import plotly.offline as py
import plotly.graph_objs as go
plotly.offline.init_notebook_mode(connected=True)
# pos = nx.spring_layout(graph)
pos = nx.circular_layout(graph)
Xv = [v[0] for v in pos.values()]
Yv = [v[1] for v in pos.values()]
@ytakashina
ytakashina / darknet-setup.md
Last active September 4, 2017 07:15
@ytakashina
ytakashina / jupyter-snippets.md
Last active April 8, 2018 02:36

Pandas

gb = df.groupby('category')
gb.groups
gb.get_group(1)

Debug

@ytakashina
ytakashina / numpy-snippets.md
Last active April 8, 2018 02:36
  • 全組み合わせ列挙
np.hstack([np.repeat(X, len(Y), axis=0), np.tile(Y, [len(X), 1])])
  • マハラノビス距離計算



  


  




    


          
    

      

        
        

          

              @ytakashina
          

          

            
                ytakashina
                / visualization-snippets.md
            
            

              Last active
              April 8, 2018 02:36
            

          

        

      

        

  

      

    
Networks


g = ig.Graph.Adjacency((pre_h_pred != 0).tolist())
g.es['weight'] = pre_h_pred[pre_h_pred.nonzero()]
g.vs['label'] = list(geos.values())
layt = g.layout('kk', dim=2)
N = len(pre_h_pred)
Xn = [layt[n][0] for n in range(N)]
Yn = [layt[n][1] for n in range(N)]




  


  




    


          
    

      

        
        

          

              @ytakashina
          

          

            
                ytakashina
                / gao2017estimating.ipynb
            
            

              Last active
              April 8, 2018 02:36
            

              
                追試: Gao, Weihao, et al. "Estimating mutual information for discrete-continuous mixtures." Advances in Neural Information Processing Systems. 2017.
              
          

        

      

        

  

    
    


          

    

      
      
Sorry, something went wrong. Reload?

      
Sorry, we cannot display this file.

      
Sorry, this file is invalid so it cannot be displayed.

      
    

  

 
    


  




    


          
    

      

        
        

          

              @ytakashina
          

          

            
                ytakashina
                / bayesian_networks.py
            
            

              Last active
              April 8, 2018 02:36
            

              
                Discrete bayesian_networks
              
          

        

      

        

  

    
    


        



  


  

        

          
          

        

        

          
          def counts_given_parents(adj, X):
        

        

          
              n, d = X.shape
        

        

          
              states_list = [set(col) for col in X.T]
        

        

          
              pstates_list = [Counter(map(tuple, X[:, adj.T[i]])).keys() for i in range(d)]
        

        

          
              counts = {i: {j: {k: np.count_nonzero(X[:, i] == k)
        

        

          
                                for k in states_list[i]}
        

        

          
                            for j in pstates_list[i]}
        

        

          
                        for i in range(d)}
        

        

          
              return counts