import pandas as pd import numpy as np import sklearn from sklearn.neural_network import MLPClassifier from sklearn.model_selection import train_test_split df=pd.read_csv("D:/Users/dell/Desktop/taxDetection.csv") data=np.array(df) x=data[:,:11] y=data[:,11] #axis=0表示输出矩阵是1行，也就是求每一列的平均值 x_mean = x.mean(axis =0) x_std = x.std(axis=0) x_scaled = (x-x_mean) / x_std #random_state:int 或RandomState，可选，默认None，随机数生成器的状态或种子。 X_train, X_test, y_train, y_test =train_test_split(x_scaled,y,test_size=0.25,random_state = 0,stratify=y) #hidden_layer_sizes :例如hidden_layer_sizes=(50, 50)，表示有两层隐藏层，第一层隐藏层有50个神经元，第二层也有50个神经元 #sgd：随机梯度下降 #max_iter: int，可选，默认200，最大迭代次数 a=MLPClassifier(solver='sgd',hidden_layer_sizes=(50,50,50),max_iter=3000) a.fit(X_train,y_train) print("Accuracy on training set:{:.2f}".format(a.score(X_train,y_train))) print("Accuracy on test set:{:.2f}".format(a.score(X_test,y_test)))在这段代码中加入特征工程

import time import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout import tkinter as tk from tkinter import ttk from sklearn.neural_network import MLPClassifier from sklearn.preprocessing import StandardScaler from tkinter.messagebox import showinfo # pip install pyserial import serial import serial.tools.list_ports from main import MyClass if name == 'main': detect = MyClass() def collect_data_click(): detect.on_collect_data_click()

1. 导入需要的库：time、pandas、sklearn、numpy、tensorflow、tkinter、serial 等。 2. 从 tensorflow.keras 中导入 Sequential 和 Dense 两个类，用于搭建神经网络模型。 3. 定义了一个名为 MyClass 的类，并...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPClassifier import joblib # 读取数据 Xtrain = pd.read_excel('sj_final.xlsx') ytrain = pd.read_excel('water_heater_log.xlsx') test = pd.read_excel('test_data.xlsx') # 训练集测试集区分。 x_train, x_test, y_train, y_test = Xtrain.iloc[:,5:],test.iloc[:,4:-1],ytrain.iloc[:,-1],test.iloc[:,-1] # 标准化 stdScaler = StandardScaler().fit(x_train) x_stdtrain = stdScaler.transform(x_train) x_stdtest = stdScaler.transform(x_test) # 建立模型 bpnn = MLPClassifier(hidden_layer_sizes = (17,10), max_iter = 200, solver = 'lbfgs',random_state=45) bpnn.fit(x_stdtrain, y_train) # 保存模型 joblib.dump(bpnn,'water_heater_nnet.m') print('构建的模型为：\n',bpnn) 报错如下ValueError: The feature names should match those that were passed during fit. Feature names must be in the same order as they were in fit.

这个错误提示是因为在进行模型训练时，数据集的特征名称顺序与测试数据集的特征名称顺序...x_train = x_train[x_test.columns] 这样可以将训练集中的特征按照测试集的特征顺序重新排列。然后再重新训练模型即可。

from sklearn.datasets import load_boston import numpy as np import pandas as pd data=pd.read_csv('./data_picture/chapter1/boston_house_prices.csv') data.head() from sklearn.model_selection import train_test_split X=data.drop('MEDV',axis=1) y=data['MEDV'] X=X.values y=y.values X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25) from sklearn.preprocessing import StandardScaler ss_X=StandardScaler() scaler_X=ss_X.fit(X_train) X_train=scaler_X.transform(X_train) X_test=scaler_X.transform(X_test) from sklearn.neural_network import MLPRegressor model = MLPRegressor(solver='lbfgs', hidden_layer_sizes=(15,15), random_state=1) model.fit(X_train, y_train) from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error print('训练集回归评估指标：') model_score1=model.score(X_train,y_train) print('The accuracy of train data is',model_score1) print('测试集回归评估指标：') model_score2=model.score(X_test,y_test) print('The accuracy of test data is',model_score2) y_test_predict=model.predict(X_test) mse=mean_squared_error(y_test,y_test_predict) print('The value of mean_squared_error:',mse) mae=mean_absolute_error(y_test,y_test_predict) print('The value of mean_absolute_error:',mae)解析一下这段代码

3. 从sklearn.model_selection模块中导入train_test_split函数，将数据集划分为训练集和测试集，其中训练集占75%，测试集占25%。 4. 从sklearn.preprocessing模块中导入StandardScaler类，用于对数据进行标准化处理...

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt from termcolor import colored as cl import itertools from sklearn.preprocessing import StandardScaler from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from xgboost import XGBClassifier from sklearn.neural_network import MLPClassifier from sklearn.ensemble import VotingClassifier # 定义模型评估函数 def evaluate_model(y_true, y_pred): accuracy = accuracy_score(y_true, y_pred) precision = precision_score(y_true, y_pred, pos_label='Good') recall = recall_score(y_true, y_pred, pos_label='Good') f1 = f1_score(y_true, y_pred, pos_label='Good') print("准确率:", accuracy) print("精确率:", precision) print("召回率:", recall) print("F1 分数:", f1) # 读取数据集 data = pd.read_csv('F:\数据\大学\专业课\模式识别\大作业\数据集1\data clean Terklasifikasi baru 22 juli 2015 all.csv', skiprows=16, header=None) # 检查数据集 print(data.head()) # 划分特征向量和标签 X = data.iloc[:, :-1] y = data.iloc[:, -1] # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 6. XGBoost xgb = XGBClassifier(max_depth=4) y_test = np.array(y_test, dtype=int) xgb.fit(X_train, y_train) xgb_pred = xgb.predict(X_test) print("\nXGBoost评估结果：") evaluate_model(y_test, xgb_pred)

5. 划分训练集和测试集：使用sklearn库的train_test_split()函数将数据集划分为训练集和测试集。 6. 使用XGBoost算法进行分类：使用XGBClassifier类构建XGBoost分类器，并使用fit()函数将训练集拟合到该分类器中。...

import pandas as pd import numpy as np from sklearn.neural_network import MLPRegressor from sklearn.model_selection import train_test_split from sklearn import preprocessing import matplotlib.pyplot as plt data_tr = pd.read_csv('BPdata_tr.txt') # 训练集样本 data_te = pd.read_csv('BPdata_te.txt') # 测试集样本 X=np.array([[0.0499853495508432],[0.334657078469172]]).reshape(1, -1) model = MLPRegressor(hidden_layer_sizes=(10,), activation='relu',random_state=10, max_iter=8000, learning_rate_init=0.3) # 构建模型,调用sklearn实现神经网络算法 model.fit(data_tr.iloc[:, :2], data_tr.iloc[:, 2]) # 模型训练 pre = model.predict(data_te.iloc[:, :2]) # 模型预测 pre1 = model.predict(X) err = np.abs(pre - data_te.iloc[:, 2]).mean() # 模型预测误差 print(pre,end='\n______________________________\n') print(pre1,end='\n++++++++++++++++++++++++++++++++\n') print('误差：',err)

from sklearn.model_selection import train_test_split from sklearn import preprocessing import matplotlib.pyplot as plt 2. 读取训练集和测试集数据，并将数据分为输入特征和目标输出两部分： ...

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] X = data.drop(['y'], axis=1) print('输出新的特征集数据***') print(X.head()) 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) def relu(X): output=np.maximum(0, X) return output def relu_back_propagation(derror_wrt_output,X): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,X): if activation_choose == 'relu': return relu(X) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def init(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b()

这段代码实现了一个神经网络类 NeuralNetwork，用于训练数据并进行预测。首先，通过 Pandas 库读取一个 Excel 文件中的数据，并使用 Seaborn 库绘制数据的相关性分析热力图。然后，将数据集分为训练集和测试集，用于...

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] x = data.drop(['y'], axis=1) print('输出新的特征集数据***') print(x.head()) 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) def relu(x): output=np.maximum(0, x) return output def relu_back_propagation(derror_wrt_output,x): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,x): if activation_choose == 'relu': return relu(x) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def init(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b() def set_learning_rate(self,learning_rate): self.learning_rate=learning_rate def set_num_iterations(self, num_iterations): self.num_iterations = num_iterations def set_xy(self, input, expected_output): self.x = input self.y = expected_output

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) def relu(x): output=np.maximum(0, x) return output ...

# -- coding: utf-8 -- """ Created on 2023-10-16 @author: Your Name 本代码用于对房屋交易数据进行分析，包括数据预处理、特征工程、模型训练与评估等步骤。目标是研究影响城市住宅价格的因素，并评估不同机器学习模型在预测房价方面的表现。 """ # 导入必要的库 import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.svm import SVR from sklearn.neural_network import MLPRegressor from sklearn.metrics import mean_absolute_error, mean_squared_error import shap import matplotlib.pyplot as plt import warnings import re # 忽略警告信息 warnings.filterwarnings("ignore") # 1. 数据加载与初步查看 def load_data(file_path): """ 加载数据并进行初步查看 """ data = pd.read_excel(file_path) print("数据集基本信息：") print(data.info()) print("\n数据集前5行：") print(data.head()) print("\n数据集描述性统计：") print(data.describe()) return data # 2. 数据预处理 def data_preprocessing(data): """ 数据预处理，包括删除无效数据、变量转化或拆分、数据集整合、删除极端值等 """ # 删除无效数据（如缺失值过多的行） data = data.dropna(thresh=len(data.columns) * 2 / 3, axis=0) # 变量转化或拆分 # 拆分户型信息，适应不同的格式 def extract_room_hall(x): if pd.isna(x): return np.nan, np.nan # 尝试匹配常见的格式，如 "3室1厅"、"3房间1厅"、"3房1厅" 等 room = 0 hall = 0 if '室' in x and '厅' in x: parts = x.split('厅') if len(parts) >= 2: room_part = parts[0] hall_part = parts[1].split(' ')[0] if ' ' in parts[1] else parts[1] room = room_part.split('室')[0] if '室' in room_part else 0 hall = hall_part.split('厅')[0] if '厅' in hall_part else 0 elif '房间' in x and '厅' in x: parts = x.split('厅') if len(parts) >= 2: room_part = parts[0] hall_part = parts[1].split(' ')[0] if ' ' in parts[1] else parts[1] room = room_part.sp

from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_squared_error, r2_score X = df.drop('PRICE', axis=1) y = df['...

import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from pathlib import Path excel_path = Path.cwd() / "C:/Users/Administrator/Desktop/augmented_data2.xlsx" data = pd.read_excel(excel_path, sheet_name='Sheet1') x = data[['掺氨比', '过量空气系数', '燃尽风位置', '主燃区温度']] y = data['NO排放浓度'] data[['主燃区温度']] = data[['主燃区温度']].astype(str) le = LabelEncoder() cat_cols = data.select_dtypes(include=['object']).columns for col in cat_cols: data[col] = le.fit_transform(data[col]) X = data.drop('NO排放浓度', axis=1) y = data['NO排放浓度'] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) import xgboost as xgb dtrain = xgb.DMatrix(X_train, label=y_train) dtest = xgb.DMatrix(X_test, label=y_test) params = { 'objective': 'reg:squarederror', 'eval_metric': 'rmse', 'eta': 0.1, 'max_depth': 6, 'subsample': 0.8, 'colsample_bytree': 0.8 } model = xgb.train(params, dtrain, num_boost_round=100, evals=[(dtrain, 'train'), (dtest, 'test')], early_stopping_rounds=10) y_pred = model.predict(dtest) from sklearn.metrics import mean_squared_error, r2_score from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split print(f"MSE: {mean_squared_error(y_test, y_pred):.2f}") print(f"RMSE: {mean_squared_error(y_test, y_pred, squared=False):.2f}") print(f"R²: {r2_score(y_test, y_pred):.2%}") import matplotlib.pyplot as plt plt.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体为黑体 plt.rcParams['axes.unicode_minus'] = False xgb.plot_importance(model) plt.show() param_constraints = { '掺氨比': (0.0, 0.5), # 氨燃料掺混比例 '过量空气系数': (1.0, 1.5), # 空燃比 '燃尽风位置': (0.0, 1.0), '主燃区温度': (1200, 1600) # 温度范围(℃) } import numpy as np from scipy.optimize import minimize def predict_no_emission(params): """ 将优化参数转换为模型输入格式 """ input_df = pd.DataFrame([params], columns=X.columns) return model.predict(xgb.DMatrix(input_df))[0] print("现有参数约束键:", param_constraints.keys()) # 初始猜测值（取训练数据均值） initial_guess = X.mean().values bounds = [ param_constraints['掺氨比'], param_constraints['过量空气系数'], (min(X['燃尽风位置']), max(X['燃尽风位置'])), # 离散型参数的编码范围 param_constraints['主燃区温度'] ] # 使用SLSQP算法进行优化 result = minimize( fun=predict_no_emission, x0=initial_guess, method='SLSQP', bounds=bounds, options={'maxiter': 100, 'ftol': 1e-6} ) # 将编码后的类别值还原 optimized_params = result.x # 燃尽风位置解码 if '燃尽风位置' in cat_cols: position_idx = X.columns.get_loc('燃尽风位置') optimized_params[position_idx] = le.inverse_transform( [int(round(optimized_params[position_idx]))] )[0] # 显示最终优化结果 print(f""" 最小NO排放浓度预测值：{result.fun:.2f} mg/m³ 对应最优参数组合：掺氨比：{optimized_params[0]:.3f} 过量空气系数：{optimized_params[1]:.2f} 燃尽风位置：{optimized_params[2]} 主燃区温度：{optimized_params[3]:.0f}℃ """) 改成其他算法

from sklearn.neural_network import MLPRegressor from sklearn.pipeline import make_pipeline # 构建神经网络管道 mlp = make_pipeline( StandardScaler(), MLPRegressor( hidden_layer_sizes=(64,32), ...

#encoding=utf8 import os import random import numpy as np import pandas as pd from sklearn.neural_network import MLPClassifier if os.path.exists('./实验/实验2 - 神经网络/第3关：使用pytorch搭建卷积神经网络识别手写数字/result.csv'): os.remove('./实验/实验2 - 神经网络/第3关：使用pytorch搭建卷积神经网络识别手写数字/result.csv') # 实现方向传播算法 #***** Begin # # End *****#

from sklearn.model_selection import train_test_split # 创建虚拟数据集 X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42) # 划分训练集和测试...

--------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) Cell In[1], line 22 20 from sklearn.preprocessing import StandardScaler 21 from sklearn.feature_extraction.image import extract_patches_2d ---> 22 from skimage.feature import hog 23 from scipy.ndimage import gaussian_filter, map_coordinates 24 import seaborn as sns ModuleNotFoundError: No module named 'skimage'

from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.svm import SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, ...

Use the credictcard-reduced.csv dataset ([Data description] and build Five classification models. Please plot confusion matrix, and evaluate your model performance using accuracy, precision, recall, F-score (70 points). A list of classification models can be found我现在需要完成上面的任务。已知导入的数据是这样的 Time V1 V2 V3 V4 V5 V6 V7 V8 V9 ... V21 V22 V23 V24 V25 V26 V27 V28 Amount Class 0 406 -2.312227 1.951992 -1.609851 3.997906 -0.522188 -1.426545 -2.537387 1.391657 -2.770089 ... 0.517232 -0.035049 -0.465211 0.320198 0.044519 0.177840 0.261145 -0.143276 0.00 1 names = [ "Nearest Neighbors", "Linear SVM", "RBF SVM", "Gaussian Process", "Decision Tree", "Random Forest", "Neural Net", "AdaBoost", "Naive Bayes", "QDA", ] classifiers = [ KNeighborsClassifier(3), SVC(kernel="linear", C=0.025, random_state=42), SVC(gamma=2, C=1, random_state=42), GaussianProcessClassifier(1.0 * RBF(1.0), random_state=42), DecisionTreeClassifier(max_depth=5, random_state=42), RandomForestClassifier( max_depth=5, n_estimators=10, max_features=1, random_state=42 ), MLPClassifier(alpha=1, max_iter=1000, random_state=42), AdaBoostClassifier(random_state=42), GaussianNB(), QuadraticDiscriminantAnalysis(), ] X, y = make_classification( n_features=2, n_redundant=0, n_informative=2, random_state=1, n_clusters_per_class=1 ) rng = np.random.RandomState(2) X += 2 * rng.uniform(size=X.shape) linearly_separable = (X, y) datasets = [ make_moons(noise=0.3, random_state=0), make_circles(noise=0.2, factor=0.5, random_state=1), linearly_separable, ] figure = plt.figure(figsize=(27, 9)) i = 1 # iterate over datasets for ds_cnt, ds in enumerate(datasets): # preprocess dataset, split into training and test part X, y = ds X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.4, random_state=42 ) x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5 # just plot the dataset first cm = plt.cm.RdBu cm_bright = ListedColormap(["#FF0000", "#0000FF"]) ax = plt.subplot(len(datasets), len(classifiers) + 1, i) if ds_cnt == 0: ax.set_title("Input data") # Plot the training points ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k") # Plot the testing points ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6, edgecolors="k" ) ax.set_xlim(x_min, x_max) ax.set_ylim(y_min, y_max) ax.set_xticks(()) ax.set_yticks(()) i += 1 # iterate over classifiers for name, clf in zip(names, classifiers): ax = plt.subplot(len(datasets), len(classifiers) + 1, i) clf = make_pipeline(StandardScaler(), clf) clf.fit(X_train, y_train) score = clf.score(X_test, y_test) DecisionBoundaryDisplay.from_estimator( clf, X, cmap=cm, alpha=0.8, ax=ax, eps=0.5 ) # Plot the training points ax.scatter( X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright, edgecolors="k" ) # Plot the testing points ax.scatter( X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, edgecolors="k", alpha=0.6, ) ax.set_xlim(x_min, x_max) ax.set_ylim(y_min, y_max) ax.set_xticks(()) ax.set_yticks(()) if ds_cnt == 0: ax.set_title(name) ax.text( x_max - 0.3, y_min + 0.3, ("%.2f" % score).lstrip("0"), size=15, horizontalalignment="right", ) i += 1 plt.tight_layout() plt.show() 老师给的模板是这样的，请帮我写类似的代码

from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay from ...

# -- coding: utf-8 -- #Z为计算得到的十二个指标数据以.npy文件保存读取出来的结果 #对得到的Z进行空值处理 #判断指标之间是否存在较强的相关性 #进行标准化处理标准化 #进行主成分分析处理 #构建多元线性回归模型 #非线性神经网络模型构建任务定价模型 #根据上面的模型计算成本增加额R1和任务完成增加量R2 def return_values(): #根据计算出指标Z，以及构建的定价模型重新对任务重新定价 import pandas as pd #导入pandas库 import numpy as np #导入nmypy库 import math #导入数学函数模 import fun #导入定义的函数 A=pd.read_excel('附件一：已结束项目任务数据.xls') Z=np.load('Z.npy') return(R1,R2) 完整代码

from sklearn.model_selection import train_test_split def return_values(): # 导入数据 A = pd.read_excel('附件一：已结束项目任务数据.xls') Z = np.load('Z.npy') # 计算得到的十二个指标数据 # 数据...

AttributeError: 'DataFrame' object has no attribute 'data'. Did you mean: '_data'?

from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import Ridge from sklearn.neural_network import MLPRegressor from ...

python多分类手写数字识别实验：所给数据一共有42000张灰度图像（分辨率为2828），以train_data.csv文件给出. 图像内容涵盖了10个手写数字0-9。另有1000条未知标签的测试数据图片（分辨率也为2828），保存在“test_data.csv”文件中。每一行为给定图片的灰度值，而每张图片代表的数值待建模识别。将预测结果保存在名为“preds.txt”的文本文件中，内容为1000行，每一行只有0-9数字中的一个数字，代表你的算法对测试数据的预测结果。预测数据顺序须与测试集“test_data.csv”中的样本顺序保持一致。写出代码

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from sklearn.neural_network import MLPClassifier 接着，我们需要读取训练数据和测试数据： python ...

数据集及说明见附件（readme.md seeds.csv），使用Sklearn库，构造神经网络模型。给出模型的评估性能,给出python代码

from sklearn.model_selection import train_test_split from sklearn.neural_network import MLPClassifier from sklearn.metrics import accuracy_score # 读取数据集 data = pd.read_csv('seeds.csv') 接...

用python代码解决以下问题：题目：微电网稳定性评估描述：微电网是指由多个分布式电源、负载和能量储存设备等组成的小型电力系统。为了提高微电网的稳定性和可靠性，我们需要对其进行稳定性评估。假设我们有一些微电网的实验数据，包括电流、电压、功率、频率等特征，以及微电网的稳定性标签（0表示不稳定，1表示稳定）。我们的目标是使用这些特征来训练一个多层神经网络模型，能够对微电网的稳定性进行分类。要求： 1. 从文件中读入数据，并通过代码分出测试集和训练集，其中测试集占总数据集的20%。 2. 使用自定义函数对数据进行正规化，使得数据的每个特征均值为0，标准差为1。 3. 利用多层神经网络(MLPClassifier)实现分类。 4. 在准确率(accuracy)、精确率(precision)、召回率(recall)、F1值(f1_score)等指标上评估模型性能，将结果打印后，同时输出到文件中，该文件命名为Performance.txt。 5. 使用numpy库来处理数据格式，使用scikit-learn库来进行机器学习模型训练和评估。

from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score 接着，读入数据集并进行分割： python data = pd.read_csv...

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