@@ -0,0 +1,49 @@
+COLOR_FIGURE = True
+
+from matplotlib import pyplot as plt
+from sklearn.datasets import load_iris
+
+data = load_iris()
+features = data['data']
+feature_names = data['feature_names']
+
+species = data['target_names'][data['target']]
+setosa = (species == 'setosa')
+
+# virginicaとversicolorの2つを比較するためのsetosaを除外
+features = features[~setosa]
+species = species[~setosa]
+virginica = species == 'virginica'
+
+# petal-widthの境界値
+t = 1.75
+
+# 3:petal-width, 2:petal-length
+p0, p1 = 3, 2
+
+if COLOR_FIGURE:
+    area1c = (1., .8, .8)
+    area2c = (.8, .8, 1.)
+else:
+    area1c = (1., 1, 1)
+    area2c = (.7, .7, .7)
+
+# petal width,length の最大小値を取得
+x0, x1 = ([features[:, p0].min() * .9, features[:, p0].max() * 1.1])
+y0, y1 = ([features[:, p1].min() * .9, features[:, p1].max() * 1.1])
+
+# 境界値で領域に色を付ける
+plt.fill_between([t, x1], [y0, y0], [y1, y1], color=area2c)
+plt.fill_between([x0, t], [y0, y0], [y1, y1], color=area1c)
+
+plt.plot([t, t], [y0, y1], 'k--', lw=2)
+plt.plot([t - .1, t - .1], [y0, y1], 'k:', lw=2)
+plt.scatter(features[virginica, p0], features[virginica, p1], c='b', marker='o')
+plt.scatter(features[~virginica, p0], features[~virginica, p1], c='r', marker='x')
+
+plt.ylim(y0, y1)
+plt.xlim(x0, x1)
+plt.xlabel(feature_names[p0])
+plt.ylabel(feature_names[p1])
+plt.savefig('../1400_02_02.png')
+