Masa Nakamura masayang

## 3x.py
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt

X = np.array([[0, 0, 1], [1, 1, 2], [2, 2, 3], [3, 3, 3.1]])
Y = np.array([0.1, 1.1, 1.8, 2.7])

regr = linear_model.LinearRegression()
regr.fit(X, Y)

## readfromtxt.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn import naive_bayes

x = np.genfromtxt("source.csv", delimiter=",")

plt.scatter(x[:,0], x[:,1])
plt.savefig("readfromtxt.png")

## bayes_simple.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB

x1 = np.genfromtxt("class1.csv", delimiter = ",")
x2 = np.genfromtxt("class2.csv", delimiter = ",")

y1 = np.zeros(x1.shape[0])
y2 = np.ones(x2.shape[0])

## instruction.txt
http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

上記にあるデータをiris.dataとして保存

## Graphvizインストールと実行.txt
$ sudo apt-get install graphviz
$ dot -T png -o decisiontree_simple.png decisiontree_simple.graphviz

## decisiontree_depth3.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn import tree

x1 = np.genfromtxt("class1.csv", delimiter = ",")
x2 = np.genfromtxt("class2.csv", delimiter = ",")

y1 = np.zeros(x1.shape[0])
y2 = np.ones(x2.shape[0])

## randomforest_simple.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier

x1 = np.genfromtxt("class1.csv", delimiter = ",")
x2 = np.genfromtxt("class2.csv", delimiter = ",")

y1 = np.zeros(x1.shape[0])
y2 = np.ones(x2.shape[0])

## class3.csv

          
            3.944800674823166098e+00
            4.996271186415386367e+00

            
              5.547242838038199508e+00
              5.414293869669647208e+00

            
              4.620646723315672943e+00
              4.911930531086467155e+00

            
              4.846418245516238343e+00
              3.584170537988319083e+00

            
              4.990222033288614689e+00
              5.167624961159549279e+00

            
              5.832925679378353045e+00
              6.730998187374821917e+00

            
              4.269651750644930743e+00
              5.684499994023479275e+00

            
              6.382861274845375021e+00
              5.195463145627869039e+00

            
              5.236782210357330491e+00
              3.926700967069445269e+00

            
              4.672536804208451855e+00
              4.868250521526301888e+00

## poly_svc.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm

x1 = np.genfromtxt("class1.csv", delimiter = ",")
x2 = np.genfromtxt("class2.csv", delimiter = ",")
x3 = np.genfromtxt("class3.csv", delimiter = ",")

y1 = np.zeros(x1.shape[0])
y2 = np.ones(x2.shape[0])

## kn_10.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier

x1 = np.genfromtxt("class1.csv", delimiter = ",")
x2 = np.genfromtxt("class2.csv", delimiter = ",")
x3 = np.genfromtxt("class3.csv", delimiter = ",")

y1 = np.zeros(x1.shape[0])
y2 = np.ones(x2.shape[0])
	import numpy as np
	from sklearn import linear_model
	import matplotlib.pyplot as plt

	X = np.array([[0, 0, 1], [1, 1, 2], [2, 2, 3], [3, 3, 3.1]])
	Y = np.array([0.1, 1.1, 1.8, 2.7])

	regr = linear_model.LinearRegression()
	regr.fit(X, Y)
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn import naive_bayes

	x = np.genfromtxt("source.csv", delimiter=",")

	plt.scatter(x[:,0], x[:,1])
	plt.savefig("readfromtxt.png")
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.naive_bayes import GaussianNB

	x1 = np.genfromtxt("class1.csv", delimiter = ",")
	x2 = np.genfromtxt("class2.csv", delimiter = ",")

	y1 = np.zeros(x1.shape[0])
	y2 = np.ones(x2.shape[0])
	http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data

	上記にあるデータをiris.dataとして保存
	$ sudo apt-get install graphviz
	$ dot -T png -o decisiontree_simple.png decisiontree_simple.graphviz
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn import tree

	x1 = np.genfromtxt("class1.csv", delimiter = ",")
	x2 = np.genfromtxt("class2.csv", delimiter = ",")

	y1 = np.zeros(x1.shape[0])
	y2 = np.ones(x2.shape[0])
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.ensemble import RandomForestClassifier

	x1 = np.genfromtxt("class1.csv", delimiter = ",")
	x2 = np.genfromtxt("class2.csv", delimiter = ",")

	y1 = np.zeros(x1.shape[0])
	y2 = np.ones(x2.shape[0])
	3.944800674823166098e+00	4.996271186415386367e+00
	5.547242838038199508e+00	5.414293869669647208e+00
	4.620646723315672943e+00	4.911930531086467155e+00
	4.846418245516238343e+00	3.584170537988319083e+00
	4.990222033288614689e+00	5.167624961159549279e+00
	5.832925679378353045e+00	6.730998187374821917e+00
	4.269651750644930743e+00	5.684499994023479275e+00
	6.382861274845375021e+00	5.195463145627869039e+00
	5.236782210357330491e+00	3.926700967069445269e+00
	4.672536804208451855e+00	4.868250521526301888e+00
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn import svm

	x1 = np.genfromtxt("class1.csv", delimiter = ",")
	x2 = np.genfromtxt("class2.csv", delimiter = ",")
	x3 = np.genfromtxt("class3.csv", delimiter = ",")

	y1 = np.zeros(x1.shape[0])
	y2 = np.ones(x2.shape[0])
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.neighbors import KNeighborsClassifier

	x1 = np.genfromtxt("class1.csv", delimiter = ",")
	x2 = np.genfromtxt("class2.csv", delimiter = ",")
	x3 = np.genfromtxt("class3.csv", delimiter = ",")

	y1 = np.zeros(x1.shape[0])
	y2 = np.ones(x2.shape[0])