给出各拟合曲线的误差MSE：import numpy as np import pandas as pd import matplotlib.pyplot as plt from scipy.stats import zscore import numpy as np from sklearn import linear_model from sklearn.preprocessing import PolynomialFeatures data = np.loadtxt('tb.txt', delimiter=',') # a=data[:,0] area = data[:, 0] price = data[:, 1] length = len(area) area = np.array(area).reshape([length, 1]) price = np.array(price) minx = min(area) maxx = max(area) x = np.arange(minx, maxx).reshape([-1, 1]) poly=PolynomialFeatures(degree=2) poly3=PolynomialFeatures(degree=3) poly4=PolynomialFeatures(degree=4) #poly5=PolynomialFeatures(degree=5) area_poly=poly.fit_transform(area) area_poly3=poly3.fit_transform(area) area_poly4=poly4.fit_transform(area) linear2 = linear_model.LinearRegression() linear2.fit(area_poly, price) linear3 = linear_model.LinearRegression() linear3.fit(area_poly3, price) linear4 = linear_model.LinearRegression() linear4.fit(area_poly4, price) #查看回归方程系数 print('Cofficients:',linear4.coef_) #查看回归方程截距 print('intercept',linear4.intercept_) plt.scatter(area, price, color='red') plt.plot(x, linear2.predict(poly.fit_transform(x)), color='blue') plt.plot(x, linear3.predict(poly3.fit_transform(x)), linestyle='--') plt.plot(x, linear4.predict(poly4.fit_transform(x)), linestyle='-.') plt.legend(['degree=0','degree=2','degree=3','degree=4']) plt.xlabel('Year') plt.ylabel('Price') plt.show() # 2022 year_2022 = np.array([[2022]]) area_2022_poly = poly.transform(year_2022) area_2022_poly3 = poly3.transform(year_2022) area_2022_poly4 = poly4.transform(year_2022) price_2022_degree2 = linear2.predict(area_2022_poly) price_2022_degree3 = linear3.predict(area_2022_poly3) price_2022_degree4 = linear4.predict(area_2022_poly4) print("Predicted price in 2022 (degree=2):", price_2022_degree2[0]) print("Predicted price in 2022 (degree=3):", price_2022_degree3[0]) print("Predicted price in 2022 (degree=4):", price_2022_degree4[0]) # 2023 year_2023 = np.array([[2023]]) area_2023_poly = poly.transform(year_2023) area_2023_poly3 = poly3.transform(year_2023) area_2023_poly4 = poly4.transform(year_2023) price_2023_degree2 = linear2.predict(area_2023_poly) price_2023_degree3 = linear3.predict(area_2023_poly3) price_2023_degree4 = linear4.predict(area_2023_poly4) print("Predicted price in 2023 (degree=2):", price_2023_degree2[0]) print("Predicted price in 2023 (degree=3):", price_2023_degree3[0]) print("Predicted price in 2023 (degree=4):", price_2023_degree4[0])

解释这段代码import numpy as np import pandas as pd from datetime import datetime from scipy.stats import skew from scipy.special import boxcox1p from scipy.stats import boxcox_normmax from sklearn.linear_model import ElasticNetCV, LassoCV, RidgeCV, Ridge from sklearn.ensemble import GradientBoostingRegressor from sklearn.svm import SVR from sklearn.pipeline import make_pipeline from sklearn.preprocessing import RobustScaler, StandardScaler from sklearn.model_selection import KFold, cross_val_score from sklearn.metrics import mean_squared_error as mse from sklearn.metrics import make_scorer from sklearn.neighbors import LocalOutlierFactor from sklearn.linear_model import LinearRegression from mlxtend.regressor import StackingCVRegressor # from xgboost import XGBRegressor # from lightgbm import LGBMRegressor import matplotlib.pyplot as plt import seaborn as sns

这段代码是在Python中导入所需要的库和模块...- import matplotlib.pyplot as plt：导入matplotlib库，并将其简写为plt，用于绘制图形。 - import seaborn as sns：导入seaborn库，并将其简写为sns，用于绘制图形。

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()

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： ...

from sklearn.model_selection import train_test_split,cross_val_score from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor from sklearn.metrics import classification_report # 绘制图形 import matplotlib.pyplot as plt import seaborn as sns # 引入回归模型 from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score

import matplotlib.pyplot as plt import seaborn as sns from sklearn.linear_model import LinearRegression from sklearn.metrics import mean_squared_error, r2_score 其中，sklearn包含了许多机器学习...

改正下列代码：import numpy as np import matplotlib.pyplot as plt from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error # 输入自变量和因变量 X = np.array([7.36, 7.37, 7.37, 7.39, 7.4 ]).reshape(-1, 1) y = np.array([ 7.37, 7.37, 7.39, 7.4, 7.41]) # 创建并训练人工神经网络模型 model = MLPRegressor(hidden_layer_sizes=(50, 50), activation='relu', solver='adam') model.fit(X, y) # 预测新的自变量对应的因变量 X_new = np.array([7.41]).reshape(-1, 1) y_pred = model.predict(X_new) print(y_pred) # 计算均方误差（MSE） mse = mean_squared_error(y, y_pred) # 计算均方根误差（RMSE） rmse = np.sqrt(mse) print("均方误差（MSE）：", mse) print("均方根误差（RMSE）：", rmse)

import matplotlib.pyplot as plt from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error # 输入自变量和因变量 X = np.array([7.36, 7.37, 7.37, 7.39, 7.4]).reshape...

import pandas as pd import numpy as np import tensorflow as tf import matplotlib.pyplot as plt #读取数据 data=pd.read_csv('Advertisement.csv') x=data.iloc[:,:-1] y=data.iloc[:,-1] print(data.head(5)) #建立模型 model=tf.keras.Sequential() #10个神经元 model.add(tf.keras.layers.Dense(10,input_shape=(3,))) model.add(tf.keras.layers.Dense(1)) model.compile(optimizer='adam',loss='mse')#optimizer = history=model.fit(x,y,epochs=2500) plt.plot(history.epoch,history.history.get('loss')) plt.show() test_data=data.iloc[:3,:-1] result=model.predict(test_data) print(result) print(data.iloc[:3,-1].values)解释每行代码

4. import matplotlib.pyplot as plt：导入 Matplotlib 库，并将其命名为 plt，以便在后面使用。 5. data=pd.read_csv('Advertisement.csv')：使用 Pandas 读取名为 Advertisement.csv 的 CSV 文件，并将其存储...

import numpy as np import pandas as pd import matplotlib.pyplot as plt from keras.models import Sequential # 导入keras from keras.layers import Dense plt.rcParams['font.sans-serif'] = 'SimHei' df = pd.read_csv("gdpcost.csv") print(df, df.shape) x_data = df.iloc[:, 1] y_data = df.iloc[:, 2] X = x_data Y = y_data plt.figure(figsize=(8, 5)) plt.scatter(X, Y) plt.show() model = Sequential() model.add(Dense(10, input_shape=(1,))) # 输出层 model.add(Dense(1)) # 定义梯度下降算法和损失函数 model.compile(optimizer='adam', loss='mse') # 训练2500次 history = model.fit(X, Y, epochs=250) # 绘制损失函数图像g plt.plot(history.epoch, history.history.get('loss')) plt.show() Y_fit = model.predict(X) w = model.layers[0].get_weights() b = model.layers[1].get_weights() print('weights:', w) print('bias:', b) # plot fit plt.scatter(X, Y, c='r', marker='s') plt.plot(X, Y_fit, c='b', marker='o') plt.legend(['蓝色：拟合点', '红色：散点图']) plt.show()请解释每行代码

1. import numpy as np: 导入NumPy库并给它起一个别名np。 2. import pandas as pd: 导入Pandas库并给它起一个别名pd。 3. import matplotlib.pyplot as plt: 导入Matplotlib库的pyplot模块并给它起一个别名...

import torch.nn as nn有什么作用

import torch.nn中的nn是PyTorch深度学习库中的一个模块，它提供了神经网络的构建和训练所需的各种工具和功能。其中nn.Module是一个基类，作为所有神经网络模块的父类，可以用于定义自定义的神经网络模型。 nn模块...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.stattools import adfuller from statsmodels.stats.diagnostic import acorr_ljungbox from arch import arch_model from pmdarima.arima import auto_arima # 读取Excel数据 data = pd.read_excel('三个-负向标准化-二分.xlsx') data2 = pd.read_excel # 将数据转换为时间序列 data['DATE'] = pd.to_datetime(data['DATE']) # data.set_index('DATE', inplace=True) data = data['F4'] # ADF检验 ADFresult = adfuller(data) print('ADF Statistic: %f' % ADFresult[0]) print('p-value: %f' % ADFresult[1]) if ADFresult[1] > 0.05: # 进行差分 diff_data = data.diff().dropna() # 再次进行ADF检验 AADFresult = adfuller(diff_data) print('ADF Statistic after differencing: %f' % AADFresult[0]) print('p-value after differencing: %f' % AADFresult[1]) data = diff_data# 计算ARIMA-GARCH组合模型的参数 model = arch_model(data, mean='AR', lags=2, vol='GARCH', p=1, o=0, q=1) AGresult = model.fit(disp='off') print(AGresult.summary())在代码后面加上计算预测值和真实值的MSE

import matplotlib.pyplot as plt from statsmodels.tsa.stattools import adfuller from statsmodels.stats.diagnostic import acorr_ljungbox from arch import arch_model from pmdarima.arima import auto_arima...

import torch.nn.functional as F

As an AI language model, I do not have the capability to import or use any external libraries. However, I can provide information on the purpose of the F module in PyTorch. The F module in ...

import pandas as pd import numpy as np import matplotlib.pyplot as plt from statsmodels.tsa.arima_model import ARIMA from sklearn.metrics import mean_squared_error import pymysql import time,os import re import requests import urllib from datetime import datetime # ---------连接-------------- connect = pymysql.connect(host='localhost', # 本地数据库 user='root', password='123456', port=3306, charset='utf8') #服务器名,账户,密码，数据库名称 cur = connect.cursor() print(cur) # 读取数据 try: select_sqli = "SELECT time,xiaoliang FROM sheji.sale where chexing='海豚';" cur.execute(select_sqli) data = pd.DataFrame(cur.fetchall(), columns=['time', 'xiaoliang']) except Exception as e: print("读取数据失败:", e) else: print("读取数据成功") # 转换时间格式 data['time'] = pd.to_datetime(data['time'], format='%Y-%m') print(data)这个代码继续怎么写

forecast_data = pd.Series(data.iloc[-1, 0], index=pd.date_range(start=data.index[-1], periods=12, freq='M')) forecast_data += pred_cumsum plt.plot(data) plt.plot(forecast_data)

import numpy as np import pandas as pd import matplotlib. pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from linear_model_03.closed_form_sol.LinearRegression_CFSol import LinearRegressionClosedFormSol # 数据2，读取数据，先进行数据转换 df = pd.read_table('../data/mpg.data', header=0) # data换成txt,即可转化txt格式 df.to_csv('../data/mpg.csv', index=False) mpg = pd. read_csv("../data/mpg.csv"). dropna() # ..表示上两级目录 X, y = np. asarray(mpg. loc[:, "horsepower"]), np. asarray(mpg. loc[:, "displacement"]) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0, shuffle=True) lr_cfs = LinearRegressionClosedFormSol(fit_intercept=True, normalized=True) # 默认训练偏置项和进行标准化 lr_cfs.fit(X_train, y_train) # 训练模型 theta = lr_cfs.get_params() print(theta) y_test_pred = lr_cfs.predict(x_test=X_test) mse, r2, r2c = lr_cfs.cal_mse_r2(y_test, y_test_pred) print("均方误差：%.5f,判决系数：%.5f,修正判决系数：%.5f" % (mse, r2, r2c)) # lr_cfs. plt_predict(y_test, y_test_pred, is_sort=False) lr_cfs.plt_predict(y_test, y_test_pred, is_sort=True) plt. figure(figsize=(7,5)) plt. plot(X_test, y_test, "ro", label="Test Samples") plt. plot(X_test, y_test_pred, "k-", lw=1.5, label="Fit Model") plt. legend(frameon=False) plt. show()

import matplotlib.pyplot as plt from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from linear_model_03.closed_form_sol.LinearRegression_CFSol import ...

cannot import name 'compare_mse' from 'skimage.measure'

这个错误通常表示您正在尝试从 skimage.measure 模块中导入一个名为 compare_mse ...from skimage.metrics import compare_mse 如果您仍然无法解决问题，请提供更多的上下文和代码，以便我能够更好地帮助您。

from sklearn.linear_model import LinearRegression怎么看线性拟合的效果

在使用 scikit-learn 进行线性回归拟合后，你可以使用以下两个指标来评估模型的效果： 1. R方值（R-squared）：它表示拟合数据的可靠程度，值越接近1越好。你可以使用模型的 score() 方法来计算 R方值。 python...

给下列代码增加预测的误差分析：import numpy as np from sklearn.neural_network import MLPRegressor #输入自变量和因变量 X = np.array([7.36, 7.37, 7.37, 7.39, 7.4]).reshape(-1, 1) y = np.array([7.37, 7.37, 7.39, 7.4, 7.41]) #创建并训练人工神经网络模型 model = MLPRegressor(hidden_layer_sizes=(50, 50), activation='relu', solver='adam') model.fit(X, y) #预测新的自变量对应的因变量 X_new = np.array([7.41]).reshape(-1, 1) y_pred = model.predict(X_new) print(y_pred)

import numpy as np from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_squared_error # 输入自变量和因变量 X = np.array([7.36, 7.37, 7.37, 7.39, 7.4]).reshape(-1, 1) y = ...

from sklearn.metrics import mean_squared_error, r2_scorefrom sklearn.linear_model import LinearRegression

import numpy as np from sklearn.metrics import mean_squared_error, r2_score from sklearn.linear_model import LinearRegression # Reading the dataset data = pd.read_csv('dataset.csv') # Splitting the ...

能不能再上面写的代码上加上决定系数，均方误差，总偏差平方和，残差平方和的计算 import pandas as pd import numpy as np from scipy.linalg import inv # 读取数据集 data = pd.read_csv('your_dataset.csv') # 提取自变量和因变量 X = data.iloc[:, :-1].values Y = data.iloc[:, -1].values # 增加常数列 X = np.concatenate([np.ones((X.shape[0], 1)), X], axis=1) # 计算回归系数 beta = np.dot(np.dot(inv(np.dot(X.T, X)), X.T), Y) # 输出结果 print('Coefficients:', beta)

import pandas as pd import numpy as np from scipy.linalg import inv from sklearn.metrics import r2_score, mean_squared_error # 读取数据集 data = pd.read_csv('your_dataset.csv') # 提取自变量和因变量 ...

from sklearn.tree import DecisionTreeRegressor from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error import matplotlib.pyplot as plt dt_reg = DecisionTreeRegressor(max_depth=5) dt_reg.fit(x_train, y_train) dt_reg.fit(x_train, y_train) y_pred = dt_reg.predict(x_test) mse = mean_squared_error(y_test, y_pred) 为什么这个mse值只有一个，不应该每一个x都有一个MSE，总共有9个MSE值吗

在这个代码中，使用的是整个测试集的预测结果来计算出一个MSE值。虽然测试集有多个样本，但是我们只需要一个MSE来评估整个模型的性能，而不是每个样本对应一个MSE值。这个MSE值是所有测试集样本的预测误差平方和的...

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