tsutarou10

#coding: utf-8

import numpy as np
import matplotlib.pyplot as plt

def sigmoid(x,mu,s):
	a = (x - mu) / s
	return 1 / (1 + np.exp(-a))

if __name__ == "__main__":

tsutarou10

#coding: utf-8

import numpy as np
import matplotlib.pyplot as plt

def phi(x,mu,s):
	return np.exp(-(pow(x-mu,2))/(2*pow(s,2)))

if __name__ == "__main__":
	N = 1000

tsutarou10

#coding: utf-8

from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import LinearSVC,SVC
import numpy as np
from sklearn.metrics import recall_score,precision_score,f1_score
from sklearn import datasets

class SVM:
		

tsutarou10

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
import random
from matplotlib import pyplot as plt

def makeData(trainNum):
	noise = np.random.randn(trainNum) * 0.1 #make a noise
	xTrain = np.random.rand(trainNum) #training data

tsutarou10

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
from matplotlib import pyplot as plt

def function(x):
	return np.sin(x)

def diff(x,n):

tsutarou10

#!/usr/bin/env python
# -*- coding: utf-8 -*-

import numpy as np
from matplotlib import pyplot as plt

def function(x):
	return x ** 2 - 4 * x + 4

def diff(x): #数値微分

tsutarou10

#coding: utf-8

import numpy as np
import matplotlib.pyplot as plt

def norm(x,mu,sigma):
	return (1 / pow(2 * np.pi,x.shape[0]/2)) * (1 / pow(np.linalg.det(sigma),0.5)) * np.exp(-0.5 * np.dot(x - mu,np.linalg.inv(sigma)).dot(x - mu))
if __name__ == "__main__":
	x = np.linspace(-5.0, 5.0, 200)
	y = np.linspace(-5.0, 5.0, 200)

tsutarou10

#coding: utf-8

import numpy as np
import matplotlib.pyplot as plt

def norm(x,mu,sigma):
	return 1 / pow(2*np.pi*sigma,0.5) * np.exp(-0.5*pow(x-mu,2)/sigma)

if __name__ == "__main__":
	N = 1000

tsutarou10

#coding: utf-8

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
from scipy.special import gamma

N = 100
alpha = [0.1,1.0,2.0,8.0,10.0]
beta = [0.1,1.0,3.0,4.0,10.0]

tsutarou10

#coding: utf-8

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab

def bern(x,p):
	return pow(p,x) * pow(1-p,1-x)

if __name__ == "__main__":