import numpy as np import librosa from sklearn.mixture import GaussianMixture from sklearn.preprocessing import StandardScaler import os from itertools import combinations # 1. 增强特征提取 def extract_features(audio_path, n_mfcc=24, target_frames=400): y, sr = librosa.load(audio_path, sr=16000) y = librosa.effects.trim(y, top_db=25)[0] mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=n_mfcc) delta = librosa.feature.delta(mfcc) delta2 = librosa.feature.delta(mfcc, order=2) spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr) zcr = librosa.feature.zero_crossing_rate(y) features = np.vstack([ mfcc, delta, delta2, spectral_centroid / 1000, zcr * 10 ]).T if len(features) > target_frames: step = len(features) // target_frames return features[::step][:target_frames] else: return np.pad(features, ((0, target_frames - len(features)), (0, 0))) # 2. 改进的UBM训练 def train_ubm(features_list, n_components=64): # 将所有样本的特征帧拼接成二维数组 all_features = np.vstack(features_list) # 形状: (n_frames_total, n_features) scaler = StandardScaler() scaler.fit(all_features) gmm = GaussianMixture( n_components=n_components, covariance_type='diag', max_iter=500, reg_covar=1e-5, random_state=42 ) gmm.fit(scaler.transform(all_features)) return gmm, scaler # 3. 提取i-vector def extract_ivector(gmm, scaler, features): features_scaled = scaler.transform(features) posteriors = gmm.predict_proba(features_scaled) # 计算统计量 Nk = posteriors.sum(axis=0) + 1e-8 Fk = np.dot(posteriors.T, features_scaled) # 简单i-vector计算 ivector = Fk.mean(axis=0) # 取特征维度的均值 return ivector / (np.linalg.norm(ivector) + 1e-8) # ... [保持其他函数不变] ... if __name__ == "__main__": data_dir = "./data" persons = [d for d in os.listdir(data_dir) if os.path.isdir(os.path.join(data_dir, d))] # 加载数据 features_list = [

import os import numpy as np import cv2 import matplotlib.pyplot as plt from sklearn.svm import SVC from tqdm import tqdm from sklearn.preprocessing import StandardScaler from PIL import Image from skimage import feature, color, data from sklearn.preprocessing import LabelEncoder import random as rn from random import shuffle from sklearn.model_selection import cross_val_score from sklearn.model_selection import KFold, StratifiedKFold from sklearn.metrics import confusion_matrix, accuracy_score, classification_report from sklearn import metrics from sklearn.svm import LinearSVC

这段代码是用 Python 语言编写的，它使用了一些常见的机器学习库和图像处理库，如sklearn、numpy、cv2等。它的主要功能是实现对图像数据的特征提取和分类，具体来说，是通过提取图像的HOG特征并使用SVM分类器对图像...

优化这段代码# import modules 导入模块 from sklearn.model_selection import train_test_split from sklearn.decomposition import PCA import pandas as pd from sklearn import svm import numpy as np import math import matplotlib.pyplot as plt import matplotlib as mpl from matplotlib import colors from sklearn.model_selection import train_test_split from sklearn import datasets from matplotlib.colors import ListedColormap from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn.model_selection import StratifiedShuffleSplit,StratifiedKFold from sklearn.model_selection import GridSearchCV from sklearn.model_selection import GridSearchCV, LeaveOneOut, cross_val_predict from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from sklearn import svm from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import roc_auc_score from sklearn.metrics import roc_auc_score import math import datetime import multiprocessing as mp from sklearn.ensemble import StackingClassifier from sklearn.pipeline import make_pipeline from sklearn.svm import LinearSVC import random

from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.neighbors import KNeighborsClassifier from ...

import pandas as pd import os from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # 计算准确性 import numpy as np from sklearn.tree import plot_tree import matplotlib.pyplot as plt # 画图 from sklearn.metrics import confusion_matrix # 绘制混淆矩阵时使用 from sklearn.metrics import roc_curve, auc # 绘制ROC曲线时使用 from sklearn.preprocessing import label_binarize # 使用sklearn中的LabelBinarizer可以将多分类标签转化为二分类标签 from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelBinarizer

您的代码看起来像是在使用Python中的一些流行的机器学习库，如Pandas、Scikit-learn和Numpy。通过这些库，您可以进行数据处理、训练和评估机器学习模型。具体来说，您的代码中使用了以下功能： - 导入了pandas、...

from sklearn.datasets import load_boston import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LassoCV import seaborn as sns from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split

其中，load_boston函数用于加载波士顿房价数据集，LassoCV是一种线性回归模型，可以进行自动特征选择和正则化，StandardScaler用于对数据进行标准化处理，train_test_split用于将数据集划分为训练集和测试集。

import pandas as pd import numpy as np import winreg import sklearn from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.linear_model import Ridge###导入岭回归算法 from sklearn.metrics import r2_score import winreg

你已经成功导入了所需的库：pandas、numpy、winreg、sklearn以及sklearn中的LinearRegression、train_test_split、Ridge和r2_score。现在你可以使用这些库中提供的功能来进行数据处理、建模和评估等任务。如果你...

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

这段代码是在 Python 中导入了一些常用的机器学习库和模块，包括 pandas、numpy、matplotlib、sklearn 等。其中： - pandas 是 Python 中常用的数据分析库，可以用来读取和处理数据； - numpy 是 Python 中常用的...

解释如下代码：import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.datasets import make_regression from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.metrics import mean_squared_error

1. numpy：Python 数组计算库，提供了高效的数组运算功能。 2. matplotlib.pyplot：Python 绘图库，用于绘制图形和数据可视化。 3. sklearn.linear_model：Scikit-Learn 库中的线性回归模型。 4. sklearn....

import numpy as np import pandas import pandas as pd import matplotlib from sklearn import naive_bayes from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler from sklearn.preprocessing import normalize from sklearn.preprocessing import Binarizer from sklearn.impute import SimpleImputer from sklearn.preprocessing import OneHotEncoder import matplotlib.pyplot as plt from sklearn.metrics import roc_curve, auc from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix matplotlib.rc("font", family='Microsoft YaHei') data=pd.read_csv(r'D:\杂货铺\机器学习\银行数据集.csv',header=None)

这段代码导入了一系列的Python库，包括NumPy、Pandas、Matplotlib、scikit-learn等。其中，NumPy是Python科学计算的核心库，Pandas是数据处理的重要库，Matplotlib是绘图库，scikit-learn是机器学习库。接下来，使用...

import librosa import numpy as np import matplotlib.pyplot as plt import seaborn as sn import warnings from sklearn.model_selection import learning_curve from sklearn.preprocessing import label_binarize from sklearn.multiclass import OneVsRestClassifier from sklearn.metrics import roc_curve, auc

- librosa：一个用于音频信号处理的Python库，可以用于提取各种音频特征。 - numpy：一个用于科学计算的Python库，包括矩阵运算、随机数生成等功能。 - matplotlib.pyplot：一个用于绘图的Python库，可以用于...

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

- from sklearn.preprocessing import RobustScaler, StandardScaler：从sklearn.preprocessing模块中导入RobustScaler、StandardScaler函数，用于进行特征缩放。 - from sklearn.model_selection import KFold, ...

import numpy as np import matplotlib.pyplot as plt import pandas as pd import seaborn as sns from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn import metrics from sklearn import preprocessing

你给出的是 Python 的代码，我看到你正在使用一些流行的 Python 库，如 NumPy、matplotlib、pandas 和 seaborn，以及一些机器学习相关的库，如 scikit-learn。这些库都是非常有用的，能够帮助你进行数据分析、可视化...

import pandas as pd import numpy as np import scipy.stats as stats import seaborn as sns from sklearn.metrics import RocCurveDisplay from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,accuracy_score from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier请在此基础上续写代码块，要求是（1）读入数据后，选取自变量"sysBP", "diaBP","age","totChol","BMI", "heartRate", "glucose"记为X，因变量"TenYearCHD"记为y，组成新的数据集。¶

from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn import tree from sklearn.decomposition import PCA import matplotlib.pyplot as plt ...

import random import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.layers import Input, Dense from tensorflow.keras.models import Model import matp

from sklearn.preprocessing import StandardScaler # 数据标准化 #### 5. 导入深度学习库 tensorflow.keras - **推荐导入方式**： python from tensorflow import keras # 核心API from tensorflow....

写完这些代码后： import pandas as pd import numpy as np from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix,classification_report from sklearn.metrics import roc_curve import matplotlib.pyplot as plt from sklearn.linear_model import logisticRegression 出现了下面的错误 partially initialized module 'sklearn' has no attribute 'version' (most likely due to a circular import)请问这个错误怎么修改

这个错误通常是由于环境问题导致的。尝试升级 scikit-learn 库到最新版本，可以使用以下命令来升级： ...另外，确保在代码文件中没有命名为 "sklearn.py" 的文件，这可能会导致命名冲突并导致该错误。

请作为资深开发工程师，解释我给出的代码。请逐行分析我的代码并给出你对这段代码的理解。我给出的代码是：【# 导入必要的库 Import the necessary libraries import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import torch import math import torch.nn as nn from scipy.stats import pearsonr from sklearn.metrics import accuracy_score from sklearn.linear_model import LinearRegression from collections import deque from tensorflow.keras import layers import tensorflow.keras.backend as K from tensorflow.keras.layers import LSTM,Dense,Dropout,SimpleRNN,Input,Conv1D,Activation,BatchNormalization,Flatten,Permute from tensorflow.python import keras from tensorflow.python.keras.layers import Layer from sklearn.preprocessing import MinMaxScaler,StandardScaler from sklearn.metrics import r2_score from sklearn.preprocessing import MinMaxScaler import tensorflow as tf from tensorflow.keras import Sequential, layers, utils, losses from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard from tensorflow.keras.layers import Conv2D,Input,Conv1D from tensorflow.keras.models import Model from PIL import * from tensorflow.keras import regularizers from tensorflow.keras.layers import Dropout from tensorflow.keras.callbacks import EarlyStopping import seaborn as sns from sklearn.decomposition import PCA import numpy as np import matplotlib.pyplot as plt from scipy.signal import filtfilt from scipy.fftpack import fft from sklearn.model_selection import train_test_split import warnings warnings.filterwarnings('ignore')】

from sklearn.preprocessing import MinMaxScaler, StandardScaler # 数据标准化 from sklearn.linear_model import LinearRegression # 传统线性模型 from sklearn.decomposition import PCA # 特征降维 from ...

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...

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