質問編集履歴
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marker=markers[idx],
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label=c1,
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edgecolor='black')
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x_train_01_subset=x_train[(y_train==0)|(y_train==1)]
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y_train_01_subset=y_train[(y_train==0)|(y_train==1)]
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よろしくお願いします。
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```pytthon
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import numpy as np
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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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import matplotlib.pyplot as plt
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%matplotlib inline
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from matplotlib.colors import ListedColormap
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iris=datasets.load_iris()
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x=iris.data[:,[2,3]]
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y=iris.target
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x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=1,stratify=y)
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y_train_01_subset=y_train[(y_train==0)|(y_train==1)]
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lrgd=LogisticRegressionGD(eta=0.05,n_iter=1000,random_state=1)
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lrgd.fit(x_train_01_subset,y_train_01_subset)
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plot_decision_regions(x=x_train_01_subset,
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y=y_train_01_subset,
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classifier=lrgd)
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edgecolor='black')
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ここまでが関数です
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x_train_01_subset=x_train[(y_train==0)|(y_train==1)]
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y_train_01_subset=y_train[(y_train==0)|(y_train==1)]
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lrgd=LogisticRegressionGD(eta=0.05,n_iter=1000,random_state=1)
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lrgd.fit(x_train_01_subset,y_train_01_subset)
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plot_decision_regions(x=x_train_01_subset,
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y=y_train_01_subset,
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classifier=lrgd)
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質問は決定領域のコードでこの下のコードを実行すると、
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データーはプロっとされるのですが、
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決定境界を表す直線が出てきません。（グラフを二つに分ける）
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こーど
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間違えている箇所がわかりません。
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教えてください。
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plot_decision_regionsこの関数が決定領域の図です。
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特徴量は２つです。
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ｚのあたいは０か１です。
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よろしくお願いします。
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```pytthon
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iris=datasets.load_iris()
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x=iris.data[:,[2,3]]
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y=iris.target
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x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=1,stratify=y)
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x_train_01_subset=x_train[(y_train==0)|(y_train==1)]
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y_train_01_subset=y_train[(y_train==0)|(y_train==1)]
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lrgd=LogisticRegressionGD(eta=0.05,n_iter=1000,random_state=1)
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lrgd.fit(x_train_01_subset,y_train_01_subset)
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plot_decision_regions(x=x_train_01_subset,
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y=y_train_01_subset,
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classifier=lrgd)
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class LogisticRegressionGD:
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def __init__(self,eta=0.05,n_iter=100,random_state=1):
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self.eta=eta
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self.n_iter=n_iter
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self.random_state=random_state
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def fit(self,x,y):
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rgen=np.random.RandomState(self.random_state) #インスタンス
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self.w_=rgen.normal(loc=0.0,scale=0.01,size=1+x.shape[1]) #特徴量＋バイアス
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self.cost_=[]
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for i in range(self.n_iter):
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net_input=self.net_input(x)
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out_put=self.activation(net_input)
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errors=(y-out_put)
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self.w_[1:]+=self.eta*x.T.dot(errors)
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self.w_[0]+=self.eta*errors.sum()
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cost=-y.dot(np.log(out_put))-((1-y).dot(np.log(1-out_put)))
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self.cost_.append(cost)
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def net_input(self,x):
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return np.dot(x,self.w_[1:])+self.w_[0]
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def activation(self,z):
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return 1./(1.+np.exp(np.clip(z,-250,250)))
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def predict(self,x):
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return np.where(self.activation(self.net_input(x))>=0.5,1,0)
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def plot_decision_regions(x,y,classifier,test_idx=None,resolution=0.02):
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2
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title
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ｚのあたいは０か１です。
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よろしくお願いします。
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```pytthon
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def plot_decision_regions(x,y,classifier,test_idx=None,resolution=0.02):
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markers=('s','x','o','^','v')
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colors=('blue','red','lightgreen','gray','cyan')
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cmap=ListedColormap(colors[:len(np.unique(y))])
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x1_min,x1_max=x[:,0].min()-1,x[:,0].max()+1
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lrgd=LogisticRegressionGD(eta=0.05,n_iter=1000,random_state=1)
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plot_decision_regions(x=x_train_01_subset,
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y=y_train_01_subset,
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classifier=lrgd)
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classifier=lrgd)
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```
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1
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def plot_decision_regions(x,y,classifier,test_idx=None,resolution=0.02):
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markers=('s','x','o','^','v')
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colors=('blue','red','lightgreen','gray','cyan')
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cmap=ListedColormap(colors[:len(np.unique(y))])
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x1_min,x1_max=x[:,0].min()-1,x[:,0].max()+1
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