Skip to content

Instantly share code, notes, and snippets.

kenmatsu4 matsuken92

Block or report user

Report or block matsuken92

Hide content and notifications from this user.

Learn more about blocking users

Contact Support about this user’s behavior.

Learn more about reporting abuse

Report abuse
View GitHub Profile
@matsuken92
matsuken92 / ParameterGrid_test.py
Last active May 11, 2019
Demonstration of hidden class ParameterGrid on sklearn.
View ParameterGrid_test.py
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
matsuken92 / default_import.py
Last active Aug 3, 2016
frequently used libraries etc
View default_import.py
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
View test.py
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)
View Least_trimmed_squares.r
# 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) {
View 00_distributions.r
# Preparing
install.packages("Rlab")
install.packages("ggplot2")
install.packages("actuar")
install.packages("plyr")
require(plyr)
require(actuar)
require(ggplot2)
require(Rlab)
@matsuken92
matsuken92 / 01_preparation.py
Last active Aug 29, 2015
GLMM : draw graphs of mixture distributions.
View 01_preparation.py
%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)
View 01_preparation.py
%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
matsuken92 / 01_prep.py
Last active Apr 3, 2017
Graph for explanation of Standard Deviation.
View 01_prep.py
%matplotlib inline
import matplotlib.pyplot as plt
from matplotlib import animation as ani
import numpy as np
import sys
plt.style.use('ggplot')
@matsuken92
matsuken92 / 01_quantile_reg_evaluate_func.py
Created Jul 28, 2015
Drawing a evaluate function for Quantile Regression.
View 01_quantile_reg_evaluate_func.py
%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
matsuken92 / 01_draw_lenna_and_filter.py
Last active Aug 29, 2015
Draw a image of famous Lenna and filtered image.
View 01_draw_lenna_and_filter.py
%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')
You can’t perform that action at this time.