train_predict = model.predict(X_train) test_predict = model.predict(X_test) train_predict = scaler.inverse_transform(train_predict) y_train = scaler.inverse_transform([y_train]) train_predict = np.tile(train_predict, (1, 4)) #将train_predict复制4次,变为(110,4) train_predict = scaler.inverse_transform(train_predict) #进行逆归一化操作 y_train = np.tile(y_train, (1, 4)) #将y_train复制4次,变为(1, 440) y_train = scaler.inverse_transform(y_train.T) #进行逆归一化操作,并将结果reshape为(110,4) test_predict = scaler.inverse_transform(test_predict) y_test = scaler.inverse_transform([y_test])报错Traceback (most recent call last): File "C:\Users\马斌\Desktop\cnn测试\cnn改.py", line 47, in <module> train_predict = scaler.inverse_transform(train_predict) File "D:\python\python3.9.5\pythonProject\venv\lib\site-packages\sklearn\preprocessing_data.py", line 541, in inverse_transform X -= self.min_ ValueError: non-broadcastable output operand with shape (110,1) doesn't match the broadcast shape (110,4)如何修改代码
时间: 2023-12-24 12:09:16 浏览: 31
你需要将y_train的形状变为(110,4),可以使用numpy的reshape()函数。具体修改代码如下:
```
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)
train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])
train_predict = np.tile(train_predict, (1, 4)) #将train_predict复制4次,变为(110,4)
train_predict = scaler.inverse_transform(train_predict) #进行逆归一化操作
y_train = np.tile(y_train, (1, 4)) #将y_train复制4次,变为(1, 440)
y_train = scaler.inverse_transform(y_train.T) #进行逆归一化操作,并将结果reshape为(110,4)
y_train = y_train.reshape(110, 4)
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])
```
这样修改后,y_train的形状就变为了(110,4),可以进行逆归一化操作了。
相关问题
# make predictions train_predict = model.predict(X_train) test_predict = model.predict(X_test) # invert predictions train_predict = scaler.inverse_transform(train_predict) Y_train = scaler.inverse_transform([Y_train]) test_predict = scaler.inverse_transform(test_predict) Y_test = scaler.inverse_transform([Y_test])
这段代码是用于进行模型预测并反向缩放预测结果的过程:
1. 使用已经训练好的LSTM模型对训练集和测试集进行预测,得到预测结果train_predict和test_predict。
2. 将train_predict和test_predict中的数值反向缩放回原始数值范围,得到反向缩放后的train_predict和test_predict。
3. 同样地,将Y_train和Y_test也反向缩放回原始数值范围,得到反向缩放后的Y_train和Y_test。注意,这里需要将Y_train和Y_test转换成列表类型,然后再进行反向缩放。
1. 采用Python编程语言自己实现StandardScaler和MinMaxScaler这两种数据标准化方法,并用于乳腺癌数据的分类。 要求模型预测的准确率结果必须与以下sklearn的一样: from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split cancer = load_breast_cancer() X = cancer.data y = cancer.target X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.3) from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler scaler = StandardScaler() #scaler = MinMaxScaler() scaler.fit(X_train) X_train_scaled = scaler.transform(X_train) X_test_scaled = scaler.transform(X_test) from sklearn.linear_model import LogisticRegression logreg=LogisticRegression() logreg.fit(X_train_scaled, y_train) y_pred = logreg.predict(X_test_scaled) from sklearn.metrics import accuracy_score accuracy_score(y_test, y_pred)
好的,这是一个很好的问题!数据标准化是数据预处理中非常重要的一步,可以帮助我们更好地训练模型。下面是我为你提供的Python代码,可以自己实现StandardScaler和MinMaxScaler这两种数据标准化方法,并用于乳腺癌数据的分类。
```
import numpy as np
class StandardScaler:
def __init__(self):
self.mean_ = None
self.std_ = None
def fit(self, X):
self.mean_ = np.mean(X, axis=0)
self.std_ = np.std(X, axis=0)
def transform(self, X):
return (X - self.mean_) / self.std_
class MinMaxScaler:
def __init__(self):
self.min_ = None
self.max_ = None
def fit(self, X):
self.min_ = np.min(X, axis=0)
self.max_ = np.max(X, axis=0)
def transform(self, X):
return (X - self.min_) / (self.max_ - self.min_)
# 加载乳腺癌数据
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
cancer = load_breast_cancer()
X = cancer.data
y = cancer.target
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, test_size=0.3)
# 使用自己实现的StandardScaler进行数据标准化
scaler = StandardScaler()
scaler.fit(X_train)
X_train_scaled = scaler.transform(X_train)
X_test_scaled = scaler.transform(X_test)
# 使用逻辑回归进行分类
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train_scaled, y_train)
y_pred = logreg.predict(X_test_scaled)
# 计算准确率
from sklearn.metrics import accuracy_score
print("Accuracy score:", accuracy_score(y_test, y_pred))
```
这段代码中,我们分别实现了StandardScaler和MinMaxScaler这两种数据标准化方法,并且使用逻辑回归进行分类。最后,我们计算了模型预测的准确率结果,应该与sklearn的一样。