y_train = [np.array(y_train)[:, i] for i in range(7)]
时间: 2023-06-15 20:07:20 浏览: 33
这行代码是将 `y_train` 中包含的多个标签分离出来,分别存储在不同的 `numpy` 数组中,每个数组对应一个标签。假设 `y_train` 是一个形状为 `(n_samples, n_labels)` 的二维数组,其中 `n_samples` 是样本数量,`n_labels` 是标签数量。那么这行代码会生成一个包含 `n_labels` 个数组的列表,每个数组的形状都是 `(n_samples,)`,表示对应标签的所有样本的标签值。这样做可以方便地对每个标签进行单独处理。
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。
修正以下代码X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, train_size=0.7) X_fuzzy = [] for i in range(X.shape[1]): fuzzy_vals = fuzz.trimf(X[:,i], [np.min(X[:,i]), np.mean(X[:,i]), np.max(X[:,i])]) X_fuzzy.append(fuzzy_vals) X_fuzzy = np.array(X_fuzzy).T # 构建深度神经模糊网络 model = tf.keras.Sequential([ tf.keras.layers.Dense(64, activation='relu', input_dim=X_fuzzy.shape[1]), tf.keras.layers.Dense(32, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) model.fit(X_fuzzy, y, epochs=10, batch_size=32) # 训练随机森林分类器 rf_clf = RandomForestClassifier(n_estimators=100, max_depth=5) rf_clf.fit(model.predict(X_fuzzy), y) # 预测新数据点 new_data = np.random.rand(5) new_data_fuzzy = [] for i in range(new_data.shape[0]): fuzzy_val = fuzz.interp_membership(np.linspace(np.min(X[:,i]), np.max(X[:,i]), 100), fuzz.trimf(np.linspace(np.min(X[:,i]), np.max(X[:,i]), 100), [np.min(X[:,i]), np.mean(X[:,i]), np.max(X[:,i])]), new_data[i]) new_data_fuzzy.append(fuzzy_val) new_data_fuzzy = np.array(new_data_fuzzy).reshape(1,-1)
修正后的代码如下:
```python
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from scipy import stats
import tensorflow as tf
import numpy as np
import skfuzzy as fuzz
# 分割训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# 对特征进行模糊化处理
X_train_fuzzy = []
X_test_fuzzy = []
for i in range(X_train.shape[1]):
fuzzy_vals = fuzz.trimf(X_train[:,i], [np.min(X_train[:,i]), np.mean(X_train[:,i]), np.max(X_train[:,i])])
X_train_fuzzy.append(fuzzy_vals)
fuzzy_vals = fuzz.trimf(X_test[:,i], [np.min(X_train[:,i]), np.mean(X_train[:,i]), np.max(X_train[:,i])])
X_test_fuzzy.append(fuzzy_vals)
X_train_fuzzy = np.array(X_train_fuzzy).T
X_test_fuzzy = np.array(X_test_fuzzy).T
# 构建深度神经模糊网络
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_dim=X_train_fuzzy.shape[1]),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train_fuzzy, y_train, epochs=10, batch_size=32)
# 训练随机森林分类器
rf_clf = RandomForestClassifier(n_estimators=100, max_depth=5)
rf_clf.fit(model.predict(X_train_fuzzy), y_train)
# 预测新数据点
new_data = np.random.rand(5)
new_data_fuzzy = []
for i in range(new_data.shape[0]):
fuzzy_val = fuzz.interp_membership(np.linspace(np.min(X[:,i]), np.max(X[:,i]), 100),
fuzz.trimf(np.linspace(np.min(X[:,i]), np.max(X[:,i]), 100),
[np.min(X[:,i]), np.mean(X[:,i]), np.max(X[:,i])]),
new_data[i])
new_data_fuzzy.append(fuzzy_val)
new_data_fuzzy = np.array(new_data_fuzzy).reshape(1,-1)
# 使用模型和随机森林分类器进行预测
if rf_clf.predict(model.predict(new_data_fuzzy)) == 1:
print("New data belongs to class 1.")
else:
print("New data belongs to class 0.")
```
修改的部分包括:
1. 对训练集和测试集都进行了模糊化处理。
2. 修正了模型的训练,使用训练集进行训练。
3. 对新数据进行模糊化处理,并使用模型和随机森林分类器进行预测。
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