for i in np.unique(x1): mean_by_x1.append(np.mean(y[x1 == i])) for j in np.unique(x2): mean_by_x2.append(np.mean(y[x2 == j])) 解释
时间: 2024-06-02 14:12:55 浏览: 8
这段代码是用来计算两个不同变量(x1和x2)在相同取值下对应的y的均值。具体来说,代码首先使用np.unique函数找到x1和x2中的唯一值,然后对每个唯一值,使用布尔索引将y中相应位置的值选出来,再使用np.mean函数计算这些值的均值,最后将均值存入mean_by_x1和mean_by_x2这两个列表中。
换句话说,这段代码是在对x1和x2进行分组,然后计算每组对应的y的均值,以便分析x1和x2与y之间的关系。
相关问题
y_pred = np.zeros((y_train.shape[0], 3)) for tree in forest: a = [] for j in range(X_train.shape[1]): if np.median(X_train[:, j]) > np.mean(X_train[:, j]): fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])]) a.append(fuzzy_vals) fuzzy_vals = np.array(a).T y_pred += tree.predict_proba(fuzzy_vals) y_pred /= n_trees改成三分类预测代码
如果你的随机森林模型是二分类的,你需要做以下更改来进行三分类预测:
1. 修改 `y_pred` 的形状为 `(y_train.shape[0], 3)`,即有三个类别。
2. 对于每个决策树,预测的输出应该是每个类别的概率,而不是二分类的概率。所以你需要将 `tree.predict_proba(fuzzy_vals)` 改为 `tree.predict_proba(fuzzy_vals.reshape(-1, fuzzy_vals.shape[1], 1)).reshape(-1, 3)`。
3. 对于每个样本的预测值,你需要将它除以 `n_trees`,而不是 `n_trees` 乘以它。
以下是修改后的代码:
```
y_pred = np.zeros((y_train.shape[0], 3))
for tree in forest:
a = []
for j in range(X_train.shape[1]):
if np.median(X_train[:, j]) > np.mean(X_train[:, j]):
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])])
else:
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])])
a.append(fuzzy_vals)
fuzzy_vals = np.array(a).T
y_pred += tree.predict_proba(fuzzy_vals.reshape(-1, fuzzy_vals.shape[1], 1)).reshape(-1, 3)
y_pred /= n_trees
```
y_pred = np.zeros(y_train.shape) for tree in forest: a = [] for j in range(X_train.shape[1]): if np.median(X_train[:, j]) > np.mean(X_train[:, j]): fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])]) a.append(fuzzy_vals) fuzzy_vals = np.array(a).T y_pred += tree.predict_proba(fuzzy_vals)[:, 1] y_pred /= n_trees怎么改成分类数据的输出
如果要将这段代码修改为分类数据的输出,需要修改两个地方:
1. 修改y_pred的初始化:分类数据的输出应该是离散的类别标签,而不是概率值。因此,我们需要将y_pred初始化为一个长度为样本数的数组,每个元素的取值为该样本所属的类别标签。
2. 修改y_pred的计算方式:在每棵决策树中,我们需要将输出的概率转换为类别标签,然后将所有决策树的输出取平均值作为最终的预测结果。
下面是修改后的代码:
```python
y_pred = np.zeros(y_train.shape[0])
for tree in forest:
a = []
for j in range(X_train.shape[1]):
if np.median(X_train[:, j]) > np.mean(X_train[:, j]):
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])])
else:
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])])
a.append(fuzzy_vals)
fuzzy_vals = np.array(a).T
y_proba = tree.predict_proba(fuzzy_vals)
# 将概率转换为类别标签
y_tree = np.argmax(y_proba, axis=1)
y_pred += y_tree
# 将所有决策树的输出取平均值作为最终的预测结果
y_pred /= n_trees
```
注意,这里假设分类数据的类别标签是从0开始的整数。如果标签不是这种形式,需要根据实际情况进行调整。
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