B. Balanced Array

本文探讨了一个编程挑战,即如何构造一个长度为n的数组,使其前半部分元素之和等于后半部分。文章提供了问题的详细描述,输入输出格式,以及一个示例性的解决方案代码,使用C++实现。

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B. Balanced Arraytime

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limit per test:1 second
memory limit per test:256 megabytes
input:standard input
output:standard output
**

You are given a positive integer nnnn22You want to construct the array aannThe first n2n2aa22the second n2n2aa22all elements of aathe sum of the first half equals to the sum of the second half (∑i=1n2ai=∑i=n2+1nai∑i=1n2ai=∑i=n2+1naiIf there are multiple answers, you can print any. It is not guaranteed that the answer exists.You have to answer ttInputThe first line of the input contains one integer tt1≤t≤1041≤t≤104ttThe only line of the test case contains one integer nn2≤n≤2⋅1052≤n≤2⋅105nn22It is guaranteed that the sum of nn2⋅1052⋅105∑n≤2⋅105∑n≤2⋅105OutputFor each test case, print the answer — “NO” (without quotes), if there is no suitable answer for the given test case or “YES” in the first line and any suitable array a1,a2,…,ana1,a2,…,an1≤ai≤1091≤ai≤109Example
input
2
4
6
8
10
output
NO
YES
2 4 1 5
NO
YES
2 4 6 8 1 3 5 11
NO

源代码:

// An highlighted block
var foo = 'bar';
#include<iostream>
#include<string>
#include<cstring>
#include<cmath>
#include<cstdio>
#include <algorithm>
using namespace std;
int main()
{
 int t,n,i;
 cin>>t;
 while(t--)
 {
   cin>>n;
   int ll=n/2,a,p,sum=0,num=0;
   if(n==2) {cout<<"NO"<<endl;continue;} 
   if(ll%2==0){
    cout<<"YES"<<endl;
    a=2;p=1;
    for(i=0;i<ll;i++){
     cout<<a<<" ";
     sum+=a;
     a+=2;
    }
      for(i=ll;i<n;i++){
       cout<<p<<" ";
       num+=p;
       if(i==n-2)
       p=sum-num;
       else p+=2;
   }
   printf("\n");
   }
   else cout<<"NO"<<endl;
 }
 return 0; 
 } 
import os import scipy.io as sio from scipy.signal import resample, butter, lfilter import numpy as np import pandas as pd import re from collections import Counter from imblearn.over_sampling import RandomOverSampler import matplotlib.pyplot as plt import tensorflow as tf from tensorflow.keras import layers, models, regularizers from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.metrics import accuracy_score # --- 设置基础文件路径 --- # 请将此路径替换为您的“源域数据集”所在路径 BASE_PATH = r'C:\Users\bzxfsj\Desktop\E题\数据集\源域数据集' PROCESSED_DATA_FILE = 'processed_source_data.npz' BALANCED_DATA_FILE = 'balanced_source_data.npz' FINAL_FEATURES_FILE = 'final_features_enhanced.npz' ATTENTION_FEATURES_FILE = 'attention_selected_features.npz' # 定义故障类型到标签的映射 FAULT_MAP = { 'OR': 'OuterRaceFault', 'IR': 'InnerRaceFault', 'B': 'BallFault', 'N': 'Normal' } # 目标采样频率 (Hz) TARGET_FS = 32000 # 轴承参数 (根据附件1的表1,SKF6205和SKF6203) BEARING_PARAMS = { 'SKF6205': {'Nd': 9, 'd': 0.3126, 'D': 1.537}, 'SKF6203': {'Nd': 9, 'd': 0.2656, 'D': 1.122} } # --- 1. 数据预处理与样本平衡 --- def butter_lowpass_filter(data, cutoff, fs, order=5): """ 对信号应用巴特沃斯低通滤波器。 """ nyquist = 0.5 * fs normal_cutoff = cutoff / nyquist if normal_cutoff >= 1: return data b, a = butter(order, normal_cutoff, btype='low', analog=False) y = lfilter(b, a, data) return y def preprocess_and_balance_data(): """ 遍历文件夹,对所有信号进行去噪、重采样、分段、归一化和样本平衡。 """ resampled_data_list = labels = rpm_values = signal_parts = print("开始遍历文件夹并处理所有信号(DE, FE, BA)...") files_processed_count = 0 for root, dirs, files in os.walk(BASE_PATH): for file in files: if file.endswith('.mat'): file_path = os.path.join(root, file) relative_path = os.path.relpath(file_path, BASE_PATH) parts = relative_path.split(os.sep) sampling_rate = None if 'kHz' in relative_path: fs_str = ''.join(filter(str.isdigit, relative_path)) if fs_str: sampling_rate = int(fs_str) * 1000 fault_type_code = parts[1] if 'Normal' in relative_path: fault_type = 'Normal' else: fault_type = FAULT_MAP.get(fault_type_code, 'Unknown') if sampling_rate: try: mat_data = sio.loadmat(file_path) rpm = None match = re.search(r'\((\d+)rpm\)', file) if match: rpm = int(match.group(1)) else: rpm_keys = if rpm_keys and np.size(mat_data[rpm_keys]) > 0: rpm = mat_data[rpm_keys].flatten() for part in: signal_keys = [key for key in mat_data.keys() if f'{part}_time' in key or f'{part}_time_' in key] if not signal_keys: continue signal = mat_data[signal_keys].flatten() if len(signal) > 0: cutoff_freq = sampling_rate * 0.45 denoised_signal = butter_lowpass_filter(signal, cutoff_freq, sampling_rate) num_samples_resampled = int(len(denoised_signal) * TARGET_FS / sampling_rate) resampled_signal = resample(denoised_signal, num_samples_resampled) resampled_data_list.append(resampled_signal) labels.append(fault_type) rpm_values.append(rpm) signal_parts.append(part) files_processed_count += 1 except Exception as e: print(f"处理文件 {file_path} 时出错: {e}. 跳过...") print(f"\n成功处理并重采样 {files_processed_count} 个样本。") # 信号分段与归一化 segment_length = 2048 all_segments = all_labels = all_rpms = all_signal_parts = print("\n开始进行信号分段与归一化...") for i, signal in enumerate(resampled_data_list): num_segments = len(signal) // segment_length for j in range(num_segments): segment = signal[j*segment_length:(j+1)*segment_length] min_val = np.min(segment) max_val = np.max(segment) if (max_val - min_val) > 1e-6: normalized_segment = (segment - min_val) / (max_val - min_val) else: normalized_segment = segment all_segments.append(normalized_segment) all_labels.append(labels[i]) all_rpms.append(rpm_values[i]) all_signal_parts.append(signal_parts[i]) all_segments = np.array(all_segments) all_labels = np.array(all_labels) all_rpms = np.array(all_rpms) all_signal_parts = np.array(all_signal_parts) print(f"分段后总样本数:{len(all_segments)}") # 样本平衡:使用随机过采样 print("\n开始进行样本平衡...") ros = RandomOverSampler(random_state=42) X_resampled, y_resampled = ros.fit_resample(all_segments.reshape(-1, segment_length), all_labels) original_indices = ros.sample_indices_ rpms_balanced = all_rpms[original_indices] signal_parts_balanced = all_signal_parts[original_indices] print("\n样本平衡后样本分布:", Counter(y_resampled)) print(f"平衡后总样本数:{len(y_resampled)}") np.savez_compressed( BALANCED_DATA_FILE, segments=X_resampled, labels=y_resampled, rpms=rpms_balanced, signal_parts=signal_parts_balanced ) print(f"\n平衡后的数据已保存至 '{BALANCED_DATA_FILE}' 文件中。") return X_resampled, y_resampled, rpms_balanced, signal_parts_balanced # --- 2. 综合故障特征工程 --- def calculate_fault_frequencies(rpm, bearing_type): """根据RPM和轴承型号计算故障特征频率 (Hz)。""" if rpm is None: return {'BPFO': 0, 'BPFI': 0, 'BSF': 0} params = BEARING_PARAMS.get(bearing_type) if not params: return {'BPFO': 0, 'BPFI': 0, 'BSF': 0} fr = rpm / 60.0 Nd = params['Nd'] d = params['d'] D = params bpfo = fr * (Nd / 2) * (1 - d / D) bpfi = fr * (Nd / 2) * (1 + d / D) bsf = fr * (D / d) * (1 - (d / D)**2) return {'BPFO': bpfo, 'BPFI': bpfi, 'BSF': bsf} def extract_features(segment, rpm, sensor_part): """提取一个信号段的综合特征。""" # 时域特征 abs_segment = np.abs(segment) mean_abs = np.mean(abs_segment) rms_val = np.sqrt(np.mean(segment**2)) std_val = np.std(segment) peak_val = np.max(abs_segment) crest_factor = peak_val / rms_val if rms_val!= 0 else 0 form_factor = rms_val / mean_abs if mean_abs!= 0 else 0 kurtosis_val = np.mean((segment - np.mean(segment))**4) / std_val**4 if std_val!= 0 else 0 impulse_factor = peak_val / mean_abs if mean_abs!= 0 else 0 margin_factor = peak_val / np.mean(np.sqrt(abs_segment))**2 if np.mean(np.sqrt(abs_segment))**2!= 0 else 0 # 频域特征 N = len(segment) yf = np.fft.fft(segment) xf = np.fft.fftfreq(N, 1 / TARGET_FS)[:N//2] power_spectrum = 2.0 / N * np.abs(yf[0:N//2]) if np.sum(power_spectrum) > 1e-6: freq_centroid = np.sum(xf * power_spectrum) / np.sum(power_spectrum) freq_variance = np.sqrt(np.sum(((xf - freq_centroid)**2) * power_spectrum) / np.sum(power_spectrum)) else: freq_centroid = 0 freq_variance = 0 # 故障特征频率相关特征 bearing_type = 'SKF6205' if sensor_part == 'DE' else 'SKF6203' fault_freqs = calculate_fault_frequencies(rpm, bearing_type) def get_freq_feature(freq, spectrum, freqs, tolerance=5): if freq == 0: return 0 freq_indices = np.where((freqs >= freq - tolerance) & (freqs <= freq + tolerance)) return np.sum(spectrum[freq_indices]) bpfo_energy = get_freq_feature(fault_freqs, power_spectrum, xf) bpfi_energy = get_freq_feature(fault_freqs, power_spectrum, xf) bsf_energy = get_freq_feature(fault_freqs, power_spectrum, xf) features = [ mean_abs, rms_val, std_val, peak_val, crest_factor, form_factor, kurtosis_val, impulse_factor, margin_factor, freq_centroid, freq_variance, bpfo_energy, bpfi_energy, bsf_energy ] return features def process_and_extract_features(): """ 加载平衡后的数据并提取综合特征。 """ try: balanced_data = np.load(BALANCED_DATA_FILE, allow_pickle=True) segments_balanced = balanced_data['segments'] labels_balanced = balanced_data['labels'] signal_parts = balanced_data['signal_parts'] rpms = balanced_data['rpms'] print(f"\n成功加载 '{BALANCED_DATA_FILE}' 文件。") except FileNotFoundError: print(f"错误: 找不到 '{BALANCED_DATA_FILE}' 文件,请确保第一步代码已成功运行。") return None, None print("\n开始提取增强特征...") extracted_features = for i in range(len(segments_balanced)): features_vector = extract_features( segments_balanced[i], rpms[i], signal_parts[i] ) extracted_features.append(features_vector) extracted_features = np.array(extracted_features) feature_names = [ 'mean_abs', 'rms', 'std', 'peak', 'crest_factor', 'form_factor', 'kurtosis', 'impulse_factor', 'margin_factor', 'freq_centroid', 'freq_variance', 'bpfo_energy', 'bpfi_energy', 'bsf_energy' ] print("\n增强特征提取完成。") print(f"提取的特征矩阵形状: {extracted_features.shape}") np.savez_compressed( FINAL_FEATURES_FILE, features=extracted_features, labels=labels_balanced, feature_names=np.array(feature_names, dtype=object) ) print(f"增强后的特征和标签已保存至 '{FINAL_FEATURES_FILE}' 文件中。") return extracted_features, labels_balanced # --- 3. 构建与训练带注意力机制的深度神经网络 --- def build_and_train_model(): """ 加载特征数据,构建注意力模型,进行训练和评估,并进行特征选择。 """ try: final_data = np.load(FINAL_FEATURES_FILE, allow_pickle=True) features = final_data['features'] labels = final_data['labels'] feature_names = final_data['feature_names'] print(f"\n成功加载 '{FINAL_FEATURES_FILE}' 文件。") except FileNotFoundError: print(f"错误: 找不到 '{FINAL_FEATURES_FILE}' 文件,请确保第二步代码已成功运行。") return # 数据标准化与标签编码 scaler = StandardScaler() features_scaled = scaler.fit_transform(features) print("\n增强特征数据已标准化。") le = LabelEncoder() labels_encoded = le.fit_transform(labels) num_classes = len(np.unique(labels_encoded)) # 划分数据集 X_train, X_test, y_train, y_test = train_test_split( features_scaled, labels_encoded, test_size=0.3, random_state=42, stratify=labels_encoded ) # 构建注意力机制模型 input_dim = X_train.shape[1] inputs = layers.Input(shape=(input_dim,)) attention_weights = layers.Dense( input_dim, activation='sigmoid', name='attention_weights', kernel_regularizer=regularizers.l1(0.01) )(inputs) weighted_inputs = layers.Multiply()([inputs, attention_weights]) x = layers.Dense(64, activation='relu')(weighted_inputs) outputs = layers.Dense(num_classes, activation='softmax')(x) model = models.Model(inputs=inputs, outputs=outputs) # 编译模型 model.compile( optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) model.summary() # 训练注意力模型 print("\n开始训练注意力模型...") history = model.fit(X_train, y_train, epochs=10, batch_size=64, verbose=1) print("注意力模型训练完成。") # 评估模型性能 loss, accuracy = model.evaluate(X_test, y_test, verbose=0) print(f"\n模型在测试集上的准确率: {accuracy:.4f}") # 提取注意力权重并进行稀疏特征选择 attention_model = models.Model(inputs=model.input, outputs=model.get_layer('attention_weights').output) attention_scores = np.mean(attention_model.predict(features_scaled), axis=0) print("\n各特征的注意力分数(重要性):") for name, score in sorted(zip(feature_names, attention_scores), key=lambda x: x[1], reverse=True): print(f"{name}: {score:.4f}") selected_indices = np.where(attention_scores > 0.6) selected_feature_names = feature_names[selected_indices] print(f"\n已选择 {len(selected_indices)} 个重要特征: {selected_feature_names}") np.savez_compressed( ATTENTION_FEATURES_FILE, features=features[:, selected_indices], labels=labels ) print(f"稀疏特征已保存至 '{ATTENTION_FEATURES_FILE}' 文件中。") # --- 主程序执行 --- if __name__ == "__main__": # 执行第一步:数据预处理与样本平衡 segments, labels, rpms, signal_parts = preprocess_and_balance_data() # 执行第二步:综合故障特征工程 features, labels = process_and_extract_features() # 执行第三步:构建与训练注意力模型 if features is not None and labels is not None: build_and_train_model()前面的错误我都给你改正了import os import numpy as np import scipy.io as sio import re import random from scipy.signal import welch from scipy.stats import kurtosis # ---------------- 参数设置 ---------------- RAW_DATA_DIR = "source_data" PROCESSED_SOURCE_FILE = "processed_source_data.npz" BALANCED_SOURCE_FILE = "balanced_source_data.npz" FINAL_FEATURES_FILE = "enhanced_features_data.npz" # 每段信号的长度 SEGMENT_LENGTH = 1024 # 每类最大样本数 MAX_SAMPLES_PER_CLASS = 1000 # 随机种子 RANDOM_SEED = 42 random.seed(RANDOM_SEED) np.random.seed(RANDOM_SEED) # ---------------- 工具函数 ---------------- def segment_signal(signal, segment_length=SEGMENT_LENGTH):     """把长信号切成多段"""     num_segments = len(signal) // segment_length     segments = []     for i in range(num_segments):         start = i * segment_length         end = start + segment_length         segments.append(signal[start:end])     return segments def extract_features(signal, rpm=None, part="DE"):     """提取时域 + 频域 + 包含轴承频率能量特征"""     # ---- 时域特征 ----     mean_abs = np.mean(np.abs(signal))     rms = np.sqrt(np.mean(signal ** 2))     std = np.std(signal)     peak = np.max(np.abs(signal))     crest_factor = peak / rms if rms != 0 else 0     form_factor = rms / mean_abs if mean_abs != 0 else 0     kurt = kurtosis(signal)     impulse_factor = peak / mean_abs if mean_abs != 0 else 0     margin_factor = peak / (np.mean(np.sqrt(np.abs(signal))) ** 2 + 1e-8)     # ---- 频域特征 ----     f, Pxx = welch(signal, fs=12000, nperseg=256)     freq_centroid = np.sum(f * Pxx) / np.sum(Pxx)     freq_variance = np.sum(((f - freq_centroid) ** 2) * Pxx) / np.sum(Pxx)     # ---- 包含轴承频率能量特征 ----     bpfo_energy = 0     bpfi_energy = 0     bsf_energy = 0     if rpm is not None:         bpfo = rpm / 60 * 0.4         bpfi = rpm / 60 * 0.6         bsf = rpm / 60 * 0.2         bpfo_energy = np.sum(Pxx[(f > bpfo - 5) & (f < bpfo + 5)])         bpfi_energy = np.sum(Pxx[(f > bpfi - 5) & (f < bpfi + 5)])         bsf_energy = np.sum(Pxx[(f > bsf - 5) & (f < bsf + 5)])     return [         mean_abs, rms, std, peak, crest_factor, form_factor,         kurt, impulse_factor, margin_factor,         freq_centroid, freq_variance,         bpfo_energy, bpfi_energy, bsf_energy     ] # ---------------- 主流程函数 ---------------- def preprocess_and_balance_data(segment_length=SEGMENT_LENGTH):     """1. 读取原始数据,切片并保存"""     if os.path.exists(PROCESSED_SOURCE_FILE):         print(f"已存在 {PROCESSED_SOURCE_FILE},直接加载。")         data = np.load(PROCESSED_SOURCE_FILE, allow_pickle=True)         return data["segments"], data["labels"], data["rpms"], data["parts"]     print("开始处理原始数据...")     all_segments, all_labels, all_rpms, all_parts = [], [], [], []     for root, dirs, files in os.walk(RAW_DATA_DIR):         for file in files:             if file.endswith(".mat"):                 file_path = os.path.join(root, file)                 try:                     mat_data = sio.loadmat(file_path)                 except Exception as e:                     print(f"加载失败 {file_path}: {e}")                     continue                 # ---- 提取 rpm ----                 rpm = None                 match = re.search(r'\((\d+)rpm\)', file)                 if match:                     rpm = int(match.group(1))                 else:                     rpm_keys = [key for key in mat_data.keys() if 'rpm' in key.lower()]                     if rpm_keys and np.size(mat_data[rpm_keys[0]]) > 0:                         rpm = mat_data[rpm_keys[0]].flatten()[0]                     else:                         rpm = None                 # ---- 提取信号 ----                 for part in ['DE', 'FE', 'BA']:                     signal_keys = [key for key in mat_data.keys() if f'{part}_time' in key or f'{part}_time_' in key]                     if not signal_keys:                         continue                     signal = mat_data[signal_keys[0]].flatten()                     # 切片                     segments = segment_signal(signal, segment_length)                     label = os.path.basename(root)  # 文件夹名作为标签                     all_segments.extend(segments)                     all_labels.extend([label] * len(segments))                     all_rpms.extend([rpm] * len(segments))                     all_parts.extend([part] * len(segments))     # 保存     np.savez_compressed(         PROCESSED_SOURCE_FILE,         segments=np.array(all_segments, dtype=object),         labels=np.array(all_labels, dtype=object),         rpms=np.array(all_rpms, dtype=object),         parts=np.array(all_parts, dtype=object)     )     print(f"处理后的数据保存至 {PROCESSED_SOURCE_FILE}")     return all_segments, all_labels, all_rpms, all_parts def balance_data():     """2. 平衡样本数量"""     if os.path.exists(BALANCED_SOURCE_FILE):         print(f"已存在 {BALANCED_SOURCE_FILE},直接加载。")         data = np.load(BALANCED_SOURCE_FILE, allow_pickle=True)         return data["segments"], data["labels"], data["rpms"], data["parts"]     print("开始平衡数据...")     segments, labels, rpms, parts = preprocess_and_balance_data()     label_to_indices = {}     for i, label in enumerate(labels):         label_to_indices.setdefault(label, []).append(i)     balanced_segments, balanced_labels, balanced_rpms, balanced_parts = [], [], [], []     for label, indices in label_to_indices.items():         if len(indices) > MAX_SAMPLES_PER_CLASS:             indices = random.sample(indices, MAX_SAMPLES_PER_CLASS)         for idx in indices:             balanced_segments.append(segments[idx])             balanced_labels.append(labels[idx])             balanced_rpms.append(rpms[idx])             balanced_parts.append(parts[idx])     np.savez_compressed(         BALANCED_SOURCE_FILE,         segments=np.array(balanced_segments, dtype=object),         labels=np.array(balanced_labels, dtype=object),         rpms=np.array(balanced_rpms, dtype=object),         parts=np.array(balanced_parts, dtype=object)     )     print(f"平衡数据保存至 {BALANCED_SOURCE_FILE}")     return balanced_segments, balanced_labels, balanced_rpms, balanced_parts def process_and_extract_features():     """3. 提取特征"""     if os.path.exists(FINAL_FEATURES_FILE):         print(f"已存在 {FINAL_FEATURES_FILE},直接加载。")         data = np.load(FINAL_FEATURES_FILE, allow_pickle=True)         return data["features"], data["labels"], data["feature_names"]     segments_balanced, labels_balanced, rpms, signal_parts = balance_data()     print("\n开始提取增强特征...")     extracted_features = []     for i in range(len(segments_balanced)):         features_vector = extract_features(             segments_balanced[i],             rpms[i],             signal_parts[i]         )         extracted_features.append(features_vector)     extracted_features = np.array(extracted_features)     feature_names = [         'mean_abs', 'rms', 'std', 'peak', 'crest_factor', 'form_factor',         'kurtosis', 'impulse_factor', 'margin_factor',         'freq_centroid', 'freq_variance',         'bpfo_energy', 'bpfi_energy', 'bsf_energy'     ]     print("\n增强特征提取完成。")     print(f"特征矩阵形状: {extracted_features.shape}")     np.savez_compressed(         FINAL_FEATURES_FILE,         features=extracted_features,         labels=labels_balanced,         feature_names=np.array(feature_names, dtype=object)     )     print(f"增强后的特征和标签已保存至 '{FINAL_FEATURES_FILE}' 文件中。")     return extracted_features, labels_balanced, feature_names # ------------------- 主函数 ------------------- if __name__ == "__main__":     features, labels, feature_names = process_and_extract_features()     print("流程完成。") 把这个融合到你给的代码,重新给出完整的可运行的代码
09-25
signals fault_type fault_code \ 0 {'BA': array([-0.01388301, -0.05834533, -0.036... Ball B 1 {'BA': array([-0.02494944, -0.03653781, -0.053... Ball B 2 {'BA': array([ 0.07479379, 0.07934061, 0.118... Ball B 3 {'BA': array([0.00982435, 0.02149319, 0.031190... Ball B 4 {'BA': array([ 0.02465835, -0.00346758, 0.019... Ball B 5 {'BA': array([-0.04822309, -0.03739923, -0.030... Ball B 6 {'BA': array([-0.01708263, 0.00613434, 0.025... Ball B 7 {'BA': array([-0.08045546, -0.02054199, -0.042... Ball B 8 {'BA': array([ 0.05479622, -0.02563832, 0.025... Ball B 9 {'BA': array([ 0.02298952, 0.02600732, 0.033... Ball B 10 {'BA': array([0.03449239, 0.00664811, 0.044752... Ball B 11 {'BA': array([-0.00750357, 0.01881168, 0.037... Ball B 12 {'BA': array([ 0.00814649, 0.11119445, 0.141... InnerRace IR 13 {'BA': array([-0.00591928, 0.03065651, 0.137... InnerRace IR 14 {'BA': array([ 0.02033074, -0.00035128, 0.015... InnerRace IR 15 {'BA': array([ 0.05447822, 0.03258908, -0.046... InnerRace IR 16 {'BA': array([-0.05292455, 0.03865575, 0.018... InnerRace IR 17 {'BA': array([-0.08614829, -0.021688 , -0.000... InnerRace IR 18 {'BA': array([-0.06199636, -0.02936384, -0.038... InnerRace IR 19 {'BA': array([ 0.08284138, 0.08380708, 0.008... InnerRace IR 20 {'BA': array([ 0.00687566, 0.04288812, -0.006... InnerRace IR 21 {'BA': array([ 0.04895918, 0.08996107, 0.080... InnerRace IR 22 {'BA': array([-0.01889113, -0.00609564, 0.001... InnerRace IR 23 {'BA': array([0.16660231, 0.13368813, 0.118518... InnerRace IR 24 {'BA': array([ 0.03869577, 0.00936272, 0.050... OuterRace OR 25 {'BA': array([ 0.03412909, 0.06326095, 0.023... OuterRace OR 26 {'BA': array([-0.06641104, -0.02351798, 0.024... OuterRace OR 27 {'BA': array([ 0.00218562, 0.00190396, -0.005... OuterRace OR 28 {'BA': array([-0.06365598, 0.00374165, 0.026... OuterRace OR 29 {'BA': array([-0.05352636, 0.02586201, 0.036... OuterRace OR 30 {'BA': array([-0.02915423, 0.02862666, -0.005... OuterRace OR 31 {'BA': array([-0.01601493, -0.1469474 , -0.008... OuterRace OR 32 {'BA': array([-0.02933921, 0.01190411, 0.052... OuterRace OR 33 {'BA': array([-0.00659799, -0.00128666, -0.005... OuterRace OR 34 {'BA': array([ 0.02288109, -0.04186087, -0.056... OuterRace OR 35 {'BA': array([-0.03527996, -0.07604043, 0.043... OuterRace OR 36 {'BA': array([-0.04434567, 0.02606976, -0.011... OuterRace OR 37 {'BA': array([ 0.02609577, -0.02943183, -0.002... OuterRace OR 38 {'BA': array([-0.02193214, 0.03452092, 0.006... OuterRace OR 39 {'BA': array([ 0.01514817, 0.00557167, 0.039... OuterRace OR 40 {'BA': array([-0.01740123, 0.01925503, 0.026... OuterRace OR 41 {'BA': array([-0.08532102, -0.04560672, -0.042... OuterRace OR 42 {'BA': array([ 0.00872241, -0.00648733, -0.042... OuterRace OR 43 {'BA': array([-0.0584131 , 0.00419627, 0.035... OuterRace OR 44 {'BA': array([-0.00985285, 0.02846256, -0.000... OuterRace OR 45 {'BA': array([-0.00391417, -0.00222378, -0.012... OuterRace OR 46 {'BA': array([ 0.09011397, 0.01879579, 0.007... OuterRace OR 47 {'BA': array([-0.0969421 , -0.05186514, -0.013... OuterRace OR 48 {'BA': array([-0.01227974, -0.0720872 , 0.042... OuterRace OR 49 {'BA': array([ 0.02246363, 0.01199934, -0.057... OuterRace OR 50 {'BA': array([-0.13181674, -0.09503073, -0.090... OuterRace OR 51 {'BA': array([-0.05220006, -0.0353767 , -0.025... OuterRace OR fault_size load rpm file_id has_BA has_DE has_FE \ 0 0.007 0 1796.0 X118 True True True 1 0.007 1 1772.0 X119 True True True 2 0.007 2 1748.0 X120 True True True 3 0.007 3 1722.0 X121 True True True 4 0.014 0 1796.0 X185 True True True 5 0.014 1 1772.0 X186 True True True 6 0.014 2 1749.0 X187 True True True 7 0.014 3 1724.0 X188 True True True 8 0.021 0 1796.0 X222 True True True 9 0.021 1 1774.0 X223 True True True 10 0.021 2 1754.0 X224 True True True 11 0.021 3 1729.0 X225 True True True 12 0.007 0 1797.0 X105 True True True 13 0.007 1 1772.0 X106 True True True 14 0.007 2 1748.0 X107 True True True 15 0.007 3 1721.0 X108 True True True 16 0.014 0 1796.0 X169 True True True 17 0.014 1 1774.0 X170 True True True 18 0.014 2 1752.0 X171 True True True 19 0.014 3 1728.0 X172 True True True 20 0.021 0 1797.0 X209 True True True 21 0.021 1 1774.0 X210 True True True 22 0.021 2 1752.0 X211 True True True 23 0.021 3 1728.0 X212 True True True 24 0.007 0 1797.0 X156 True True True 25 0.007 1 1773.0 X158 True True True 26 0.007 2 1750.0 X159 True True True 27 0.007 3 1724.0 X160 True True True 28 0.007 0 1797.0 X144 True True True 29 0.007 1 1774.0 X145 True True True 30 0.007 2 1751.0 X146 True True True 31 0.007 3 1725.0 X147 True True True 32 0.007 0 1796.0 X130 True True True 33 0.007 1 1773.0 X131 True True True 34 0.007 2 1750.0 X132 True True True 35 0.007 3 1725.0 X133 True True True 36 0.014 0 1796.0 X197 True True True 37 0.014 1 1772.0 X198 True True True 38 0.014 2 1749.0 X199 True True True 39 0.014 3 1723.0 X200 True True True 40 0.021 0 1796.0 X258 True True True 41 0.021 1 1771.0 X259 True True True 42 0.021 2 1746.0 X260 True True True 43 0.021 3 1718.0 X261 True True True 44 0.021 0 1796.0 X246 True True True 45 0.021 1 1771.0 X247 True True True 46 0.021 2 1747.0 X248 True True True 47 0.021 3 1719.0 X249 True True True 48 0.021 0 1796.0 X234 True True True 49 0.021 1 1771.0 X235 True True True 50 0.021 2 1748.0 X236 True True True 51 0.021 3 1721.0 X237 True True True fault_type_encoded 0 0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 10 0 11 0 12 1 13 1 14 1 15 1 16 1 17 1 18 1 19 1 20 1 21 1 22 1 23 1 24 2 25 2 26 2 27 2 28 2 29 2 30 2 31 2 32 2 33 2 34 2 35 2 36 2 37 2 38 2 39 2 40 2 41 2 42 2 43 2 44 2 45 2 46 2 47 2 48 2 49 2 50 2 怎么处理数据来构建模型
09-24
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