William La Cava lacava

## timeout_handling.ipynb

      
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                lacava
                / timeout_handling.ipynb
            
            
              Created
              April 11, 2022 16:27
            
              
                Example of timeout handling for the SRBench Competition (https://cavalab.org/srbench/competition-2022/)
              
          
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## list_notebooks.py
"""
William La Cava 2020
license: GPL v3
this script connects to a server via ssh, looks for running
jupyter notebooks, and lists them.

note: for this to work, ssh needs to find the jupyter command on your
remote machine. if you have jupyter in /home/you/anaconda3/bin/,
for example, you can symlink it to /usr/local/bin to achive this:
ln -s /home/you/anaconda3/bin/jupyter /usr/local/bin/

## p_values_for_logreg.py
from sklearn import linear_model
import numpy as np
import scipy.stats as stat

class LogisticReg:
    """
    Wrapper Class for Logistic Regression which has the usual sklearn instance
    in an attribute self.model, and pvalues, z scores and estimated
    errors for each coefficient in


## x_means.py
"""
以下の論文で提案された改良x-means法の実装

クラスター数を自動決定するk-meansアルゴリズムの拡張について
http://www.rd.dnc.ac.jp/~tunenori/doc/xmeans_euc.pdf
"""

import numpy as np
from scipy import stats
from sklearn.cluster import KMeans

## simple_mlp_tensorflow.py
# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
	"""
	William La Cava 2020
	license: GPL v3
	this script connects to a server via ssh, looks for running
	jupyter notebooks, and lists them.

	note: for this to work, ssh needs to find the jupyter command on your
	remote machine. if you have jupyter in /home/you/anaconda3/bin/,
	for example, you can symlink it to /usr/local/bin to achive this:
	ln -s /home/you/anaconda3/bin/jupyter /usr/local/bin/
	from sklearn import linear_model
	import numpy as np
	import scipy.stats as stat

	class LogisticReg:
	"""
	Wrapper Class for Logistic Regression which has the usual sklearn instance
	in an attribute self.model, and pvalues, z scores and estimated
	errors for each coefficient in
	"""
	以下の論文で提案された改良x-means法の実装

	クラスター数を自動決定するk-meansアルゴリズムの拡張について
	http://www.rd.dnc.ac.jp/~tunenori/doc/xmeans_euc.pdf
	"""

	import numpy as np
	from scipy import stats
	from sklearn.cluster import KMeans
	# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
	# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

	# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
	# where x' = [x \| 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
	# Similarly, for h * W_2 + b_2
	import tensorflow as tf
	import numpy as np
	from sklearn import datasets
	from sklearn.model_selection import train_test_split