matsuken92

%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')

matsuken92

%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

%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

%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

# 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

# Preparing
install.packages("Rlab")
install.packages("ggplot2")
install.packages("actuar")
install.packages("plyr")
require(plyr)
require(actuar)
require(ggplot2)
require(Rlab)

matsuken92

\nabla f = \frac{d f({\bf x})}{d {\bf x}} = \left[ \begin{array}{r} \frac{\partial f}{\partial x_1} \\ ... \\ \frac{\partial f}{\partial x_2} \end{array} \right]

matsuken92

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

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

%matplotlib inline 
import sys
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
from matplotlib import animation as ani
import sklearn.metrics as mt