Python

python-numpy

meshgrid

根據坐標向量創建坐標矩陣

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============== ==== ==== ======= ===== ====================
                Min  Max   Mean    SD   Class Correlation
============== ==== ==== ======= ===== ====================
sepal length:   4.3  7.9   5.84   0.83    0.7826
sepal width:    2.0  4.4   3.05   0.43   -0.4194
petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)
petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)
============== ==== ==== ======= ===== ====================

上述為iris資料庫之描述性統計資料，下面取petal length及petal width使用，分別取最小值及最大值後加減1，並進行meshgrid處理
(0, 7.9, 0.1)
(-0.9, 3.5, 0.1) –> 44個區間(-0.9, -0.8, -0.7,…..3.4)
XX即為[ 0. , 0.1, 0.2, …. 7.6, 7.7, 7.8]共44個
yy即為[-0.9, -0.9, -0.9, …. -0.9, -0.9, -0.9][-0.8, -0.8, -0.8, …. -0.8, -0.8, -0.8]…..[3.4, 3.4, 3.4, …. 3.4, 3.4, 3.4]

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from itertools import product
import numpy as np
import matplotlib.pyplot as plt
from sklearn. datasets import load_iris
from sklearn import tree
iris = load_iris()
X = iris.data[:, [2, 3]]
y = iris.target
clf = tree.DecisionTreeClassifier(max_depth=2)
clf.fit(X, y)
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 #加1減1只是為了繪圖時留空白
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                     np.arange(y_min, y_max, 0.1)) #meshgrid根據坐標向量創建坐標矩陣