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
时间: 2023-06-10 14:08:57 浏览: 46
如果你要将这段代码改成三分类预测,你需要将第1行的 `y_pred = np.zeros((y_train.shape[0], 3))` 中的 `3` 改成你的类别数量,比如说是 `y_pred = np.zeros((y_train.shape[0], 3))`,表示有3个不同的类别。
然后,在第9行的 `y_pred /= n_trees` 之前,你需要将 `tree.predict_proba(fuzzy_vals)` 改成 `tree.predict(fuzzy_vals)`。这是因为 `predict_proba` 函数是用于返回每个类别的概率,而 `predict` 函数则返回每个样本的类别标签。
最后,你需要将第9行的 `y_pred /= n_trees` 改成 `y_pred /= n_trees * num_classes`,其中 `num_classes` 是你的类别数量,用来计算每个类别的平均概率。
相关问题
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。
改成三分类代码n_trees = 100 max_depth = 10 forest = [] for i in range(n_trees): idx = np.random.choice(X_train.shape[0], size=X_train.shape[0], replace=True) X_sampled = X_train[idx, :] y_sampled = y_train[idx] X_fuzzy = [] for j in range(X_sampled.shape[1]): if np.median(X_sampled[:, j])> np.mean(X_sampled[:, j]): fuzzy_vals = fuzz.trapmf(X_sampled[:, j], [np.min(X_sampled[:, j]), np.mean(X_sampled[:, j]), np.median(X_sampled[:, j]), np.max(X_sampled[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_sampled[:, j], [np.min(X_sampled[:, j]), np.median(X_sampled[:, j]), np.mean(X_sampled[:, j]), np.max(X_sampled[:, j])]) X_fuzzy.append(fuzzy_vals) X_fuzzy = np.array(X_fuzzy).T tree = RandomForestClassifier(n_estimators=1, max_depth=max_depth) tree.fit(X_fuzzy, y_sampled) forest.append(tree) inputs = keras.Input(shape=(X_train.shape[1],)) x = keras.layers.Dense(64, activation="relu")(inputs) x = keras.layers.Dense(32, activation="relu")(x) outputs = keras.layers.Dense(1, activation="sigmoid")(x) model = keras.Model(inputs=inputs, outputs=outputs) model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"]) 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 model.fit(X_train, y_pred, epochs=10, batch_size=32) y_pred = model.predict(X_test) mse = mean_squared_error(y_test, y_pred) rmse = math.sqrt(mse) print('RMSE:', rmse) print('Accuracy:', accuracy_score(y_test, y_pred))
这段代码实现了一个使用模糊隶属度将原始特征转换为模糊特征的随机森林分类器,并将其与一个神经网络模型进行集成。与之前不同的是,这里使用的是三分类问题,即目标变量有三个可能的取值。
具体来说,代码首先定义了一个包含100个树且最大深度为10的随机森林分类器,然后对每个树进行如下操作:从训练集中随机选择一个样本集,然后使用模糊隶属度将特征转换为模糊特征,并用转换后的特征和目标变量训练一个决策树。所有树训练完毕后,使用模糊隶属度将训练集特征转换为模糊特征,并对每个树的预测结果取平均。这样得到的预测结果作为神经网络的输入,再用标准的二分类交叉熵损失和Adam优化器训练神经网络。
最终,代码输出了测试集上的均方根误差和准确率。注意,这里使用的是均方根误差而不是分类准确率,因为模型输出的是概率值而不是类别标签。