写一个基本的shapelet代码
时间: 2024-03-18 19:38:47 浏览: 22
以下是一个基本的Shapelet代码示例,假设我们有一个时间序列数据集X和相应的类标签y,我们希望从中抽取代表性的Shapelet:
```
import numpy as np
def distance(x, y):
# 计算两个时间序列之间的距离
return np.sqrt(np.sum((x - y) ** 2))
def information_gain(X, y, candidate):
# 计算使用候选Shapelet进行分割后的信息增益
X_left = []
X_right = []
for i in range(len(X)):
if distance(X[i][:len(candidate)], candidate) < distance(X[i][-len(candidate):], candidate):
X_left.append(X[i])
else:
X_right.append(X[i])
y_left = y[:len(X_left)]
y_right = y[len(X_left):]
H_y = entropy(y)
H_y_left = entropy(y_left)
H_y_right = entropy(y_right)
IG = H_y - len(X_left) / len(X) * H_y_left - len(X_right) / len(X) * H_y_right
return IG
def entropy(y):
# 计算给定类标签的熵
unique_labels, counts_labels = np.unique(y, return_counts=True)
probabilities = counts_labels / len(y)
entropy = -np.sum(probabilities * np.log2(probabilities))
return entropy
def select_shapelet(X, y, max_length):
# 从X中选择一个最佳的Shapelet
best_gain = 0
best_shapelet = None
for length in range(1, max_length + 1):
for i in range(len(X)):
candidate = X[i][:length]
gain = information_gain(X, y, candidate)
if gain > best_gain:
best_gain = gain
best_shapelet = candidate
return best_shapelet
```
这个代码实现了基于信息增益的Shapelet选择算法。它使用了一个距离函数来计算两个时间序列之间的距离,以及一个计算熵的函数来评估Shapelet的分类能力。`select_shapelet`函数遍历每个长度为1到max_length的候选Shapelet,并使用`information_gain`函数计算每个候选Shapelet的信息增益。最终,它返回具有最大信息增益的Shapelet。