x = iris.data[:, [2, 3]]
y = iris.target
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.3,
random_state=1,
stratify=y)
#%%
sc = StandardScaler()
sc.fit(x_train)
x_train_std = sc.transform(x_train)
x_test_std = sc.transform(x_test)
x_combined_std = np.vstack((x_train_std, x_test_std))
y_combined = np.hstack((y_train, y_test))
#%%
knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')
knn.fit(x_train_std, y_train)
#%%
drp.plot_decision_regions(x=x_combined_std,
y=y_combined,
classifier=knn,
test_idx=range(105, 150))
plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
from layers import decisionregionplotfunction as drp
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from pydotplus import graph_from_dot_data
#%%
iris=datasets.load_iris()
x=iris.data[:,[2,3]]
y=iris.target
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=1,stratify=y)
x_combined=np.vstack((x_train,x_test))
y_combined=np.hstack((y_train,y_test))
#%%
tree=DecisionTreeClassifier(criterion='gini',max_depth=4,random_state=1)
tree.fit(x_train,y_train)
#%%
drp.plot_decision_regions(x_combined,y_combined,classifier=tree,test_idx=range(105,150))
plt.xlabel('petal length [cm]')
plt.ylabel('petal width [cm]')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
#%%
dot_data=export_graphviz(tree,filled=True,rounded=True,class_names=['Setosa','Versicolor','Virginica'],feature_names=['petal length','petal width'],out_file=None)
graph=graph_from_dot_data(dot_data)
graph.write_png('tree.png')
import numpy as np
'''
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__),'..'))
'''
import layers
from layers import classes as ccc
from layers import decisionregionplotfunction as drp
df = pd.read_csv(
'http://archive.ics.uci.edu/ml/'
'machine-learning-databases/iris/iris.data',
header=None)
df.tail()
y = df.iloc[0:100, 4].values
y = np.where(y == 'Iris-setosa', 0, 1)
x = df.iloc[0:100, [0, 2]].values
lrgd = ccc.LogisticRegressionGD(eta=0.05, n_iter=1000, random_state=1)
lrgd.fit(x, y)
drp.plot_decision_regions(x=x, y=y, classifier=lrgd)
plt.xlabel('sepal length [standardaized]')
plt.ylabel('petal length [standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
plt.show()
"""
#%%
#↓は正規化済み＋決定領域を示すグラフが出てくる（決定領域のグラフの仕組みはまだわかってない）
x_std = np.copy(x)
x_std[:, 0] = (x[:, 0] - x[:, 0].mean()) / x[:, 0].std()
x_std[:, 1] = (x[:, 1] - x[:, 1].mean()) / x[:, 1].std()
#%%
#ada=ccc.AdalineGD(n_iter=15,eta=0.01)
#ada.fit(x_std,y)
ads = ccc.AdalineGD_stochasticgradientdescent(n_iter=15, eta=0.01)
ads.fit(x_std, y)
#%%
#drp.plot_decision_regions(x_std,y,classifier=ada)
drp.plot_decision_regions(x_std, y, classifier=ads)
plt.xlabel('sepal length[standardized]')
plt.ylabel('petal length[standardized]')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
#%%
plt.plot(range(1, len(ads.cost_) + 1), ads.cost_, marker='o')
plt.xlabel('Epochs')
plt.ylabel('sumsquarederror')
plt.tight_layout()