使用numy操作的虹膜分类ipython

Question

我正在关注这本书Building Machine Learning Systems with Python .从scipy加载数据集后，我需要提取属于setosa的所有特征的索引。但我无法提取。可能是因为我没有使用numpy数组。有人可以帮我提取索引号吗？下面的代码

from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
import numpy as np
# We load the data with load_iris from sklearn
data = load_iris()
features = data['data']
feature_names = data['feature_names']
target = data['target']
for t,marker,c in zip(xrange(3),">ox","rgb"):
# We plot each class on its own to get different colored markers
plt.scatter(features[target == t,0], features[target == t,1],
            marker=marker, c=c)
plength = features[:, 2]
# use numpy operations to get setosa features
is_setosa = (labels == 'setosa')
# This is the important step:
max_setosa = plength[is_setosa].max()
min_non_setosa = plength[~is_setosa].min()
print('Maximum of setosa: {0}.'.format(max_setosa))
print('Minimum of others: {0}.'.format(min_non_setosa))

Answer 1

在问题行之前定义标签。

target_names = data['target_names']
labels = target_names[target]

现在这些行将正常工作：

is_setosa = (labels == 'setosa')
setosa_petal_length = plength[is_setosa].

额外。来自sklearn的数据堆（数据= load_iris（））由数字0-2的目标数组组成，这些数组与花的特征和手段有关。使用它，您可以提取属于setosa的所有特征（其中目标等于0），例如：

petal_length = features[:, 2]
setosa_petal_length = petal_length[target == 0]

用data['target_names']面对这个问题，你会在顶部得到两行，这是你问题的解决方案。顺便说一下，数据中的所有数组都是来自 NumPy 的 ndarray。