ytakashina

pip install tqdm numpy cupy scipy scikit-learn pandas seaborn bokeh graphviz pystan chainer tensorflow keras

ytakashina

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

これを参考に。

https://gist.github.com/filitchp/5645d5eebfefe374218fa2cbf89189aa

最後だけ以下のように。

$ cmake -D CMAKE_BUILD_TYPE=RELEASE -D INSTALL_C_EXAMPLES=OFF -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_CUDA=ON ..

ytakashina

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

• data

import tqdm
import pickle
import numpy as np
import scipy
import sklearn
import pandas as pd
import plotly

ytakashina

ytakashina

H, W = map(int, input().split())
maze = []
distances = []
n_aisle = 0
for i in range(H):
    row = input()
    maze.append(row)
    distances.append([100000000 for i in range(W)])
    n_aisle += row.count('.')
 

ytakashina

$ pycodestyle . | cut -d: -f1 | sort | uniq | xargs autopep8 -i --ignore E501

ytakashina

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

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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)]