from sklearn.preprocession import StandarScaler X = data['X'] Y = data['Y'] X.shape,y.shape scaler = StandarScaler() scaler.fit(X) X = scaler.transforn(X)

这段代码中有一个小错误，变量y的定义应该是小写的，而不是大写。修改后的代码如下：

from sklearn.preprocessing import StandardScaler
X = data['X'] 
y = data['Y'] 
X_shape = X.shape
y_shape = y.shape
scaler = StandardScaler() 
scaler.fit(X) 
X = scaler.transform(X)

除此之外，我还对代码进行了一些小改动：

1. 将`StandarScaler`改为了`StandardScaler`，因为正确的拼写是StandardScaler。

2. 将`X.shape,y.shape`改为了`X_shape = X.shape`和`y_shape = y.shape`，因为`shape`是一个属性而不是方法，应该使用`X.shape`和`y.shape`获取其形状。另外，这里将形状保存在变量中是为了方便后续使用，因为在标准化后，`X`的形状可能会发生变化。

3. 将`X = scaler.transforn(X)`改为了`X = scaler.transform(X)`，因为`transform`是一个方法而不是属性，应该使用`scaler.transform(X)`进行转换。

希望这次修改能满足你的要求。

相关问题

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd from sklearn.utils import shuffle from sklearn.preprocessing import scale df = pd.read_csv("C:\\boston.csv",header=0) ds = df.values from sklearn.datasets import load_boston boston = load_boston() X = boston.data y = boston.target from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential([ Dense(64, activation='relu', input_shape=(X_train.shape[1],)), Dense(64, activation='relu'), Dense(64, activation='relu'), Dense(1) ]) model.compile(loss='mean_squared_error', optimizer='adam') history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32) from sklearn.metrics import mean_squared_error y_pred = model.predict(x_test)mse = mean_squared_error(y_test, y_pred)print('MSE:’, mse) import matplotlib.pyplot as plt plt.plot(history.history['accuracy'], label='train') plt.plot(history.history['val_accuracy'], label='validation') plt.legend() plt.show()

这段代码使用TensorFlow和Keras实现了一个神经网络模型，用于预测波士顿房价。以下是代码的解释：

首先，导入必要的库和模块：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.utils import shuffle
from sklearn.preprocessing import scale

然后，读取波士顿房价数据集并对其进行预处理：

df = pd.read_csv("C:\\boston.csv",header=0)
ds = df.values

接着，从sklearn.datasets模块中加载波士顿房价数据集，并将其分为训练集和测试集：

from sklearn.datasets import load_boston
boston = load_boston()
X = boston.data
y = boston.target
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

对训练集和测试集进行标准化处理：

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

定义一个包含4个Dense层的神经网络模型：

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(64, activation='relu'),
    Dense(1)
])

编译模型并训练：

model.compile(loss='mean_squared_error', optimizer='adam')

history = model.fit(X_train, y_train, validation_split=0.1, epochs=100, batch_size=32)

使用模型对测试集进行预测，并计算均方误差：

from sklearn.metrics import mean_squared_error
y_pred = model.predict(x_test)
mse = mean_squared_error(y_test, y_pred)
print('MSE:’, mse)

最后，绘制模型的训练和验证准确率曲线：

import matplotlib.pyplot as plt

plt.plot(history.history['accuracy'], label='train')
plt.plot(history.history['val_accuracy'], label='validation')

plt.legend()

plt.show()

import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from sklearn.linear_model import LogisticRegression from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.metrics import classification_report from sklearn.metrics import confusion_matrix fruits = pd.read_table('fruit_data_with_colors.txt') fruits.head() fruits.shape print(fruits['fruit_name'].unique()) fruits.drop('fruit_label', axis=1).hist(bins=30, figsize=(9,9)) plt.suptitle("Histogram for each numeric input variable") plt.show() feature_names = ['mass', 'width', 'height', 'color_score'] x = fruits[feature_names] y = fruits['fruit_label'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0) scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) logreg = LogisticRegression() logreg.fit(X_train, y_train) clf = DecisionTreeClassifier().fit(X_train, y_train) knn = KNeighborsClassifier() knn.fit(X_train, y_train) svm = SVC() svm.fit(X_train, y_train) pred = knn.predict(X_test)

你给出的代码是一个简单的机器学习分类问题的示例代码，使用的是Python中的sklearn库。该代码使用了4个输入特征来预测水果的标签，使用了4个分类器：Logistic回归、决策树、K近邻和支持向量机（SVM）。其中，MinMaxScaler用于对数据进行归一化处理，train_test_split用于将数据集分为训练集和测试集。最后使用K近邻分类器进行预测，并输出预测结果。