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matsuken92 / ParameterGrid_test.py

Last active May 11, 2019 14:07

Demonstration of hidden class ParameterGrid on sklearn.

	from sklearn.model_selection._search import ParameterGrid

	param_grid = {
	"metric_list" : [None, "None", "binary_logloss", "auc", ["binary_logloss"], ["auc"],
	['binary_logloss','auc', ], ['auc', 'binary_logloss',] ],
	"first_metric_only": [True, False],
	"eval_train_metric": [True, False],
	}
	pg = ParameterGrid(param_grid)

matsuken92 / default_import.py

Last active August 3, 2016 17:37

frequently used libraries etc

	import math, sys, functools, os
	import numpy as np
	import numpy.random as rd
	from numpy import matrix
	import pandas as pd
	import scipy as sp
	from scipy import stats as st
	from datetime import datetime as dt
	from collections import Counter
	from itertools import chain

matsuken92 / test.py

Created May 5, 2016 11:12

	trial_num = 10000

	x = rd.multinomial(1, [1/6]*6, trial_num)
	result = np.sum(x, axis=0)
	data = np.array([result, np.array([1/6]6)trial_num]).T

	# Draw graph
	df = pd.DataFrame(data, columns=["trial","theory"],index=range(1,7))
	ax = df.plot.bar()
	ax.set_ylim(0,2000)

matsuken92 / Least_trimmed_squares.r

Last active November 28, 2015 14:48

	# LTS (Least Trimmed Squares) testing
	# Reference: https://cran.r-project.org/web/packages/galts/galts.pdf

	install.packages("galts")
	install.packages("ggplot2")
	require(galts)
	require(ggplot2)


	lts_test <- function(x, y) {

matsuken92 / 00_distributions.r

Last active December 5, 2015 00:50

matsuken92 / 01_preparation.py

Last active August 29, 2015 14:27

GLMM : draw graphs of mixture distributions.

	%matplotlib inline
	import matplotlib.pyplot as plt
	import numpy as np
	import scipy.stats as st
	from matplotlib import animation as ani
	plt.style.use('ggplot')
	plt.rc('text', usetex=True)

matsuken92 / 01_preparation.py

Last active August 29, 2015 14:26

	%matplotlib inline
	import matplotlib.pyplot as plt
	import numpy as np
	import scipy.stats as st
	from matplotlib import animation as ani
	plt.style.use('ggplot')
	plt.rc('text', usetex=True)

matsuken92 / 01_prep.py

Last active February 19, 2022 06:56

Graph for explanation of Standard Deviation.

	%matplotlib inline
	import matplotlib.pyplot as plt
	from matplotlib import animation as ani
	import numpy as np
	import sys
	plt.style.use('ggplot')

matsuken92 / 01_quantile_reg_evaluate_func.py

Created July 28, 2015 15:15

Drawing a evaluate function for Quantile Regression.

	%matplotlib inline
	import matplotlib.pyplot as plt
	import sys
	import numpy as np
	from matplotlib import animation as ani

	plt.style.use('ggplot')

	x = np.linspace(-2, 2, 500)
	x2 = (1/2.)x*2

matsuken92 / 01_draw_lenna_and_filter.py

Last active August 29, 2015 14:24

Draw a image of famous Lenna and filtered image.

	%matplotlib inline
	import matplotlib.pyplot as plt
	import numpy as np
	import math
	import mahotas as mh
	import os.path

	# get Lenna image
	if os.path.isfile('./lenna.jpg'):
	im = mh.demos.load('lena')