data00=data m,n = np.shape(data00) a = np.array(data00) Data00 = a[1:m,2:n] Data00 = Data00.astype(np.float64) Power = Data00[:,13] Power_train = Power[0:96] P_min = np.min(Power_train) P_gap = np.max(Power_train)-np.min(Power_train) Power_uni = (Power-P_min)/P_gap # 提取imfs和剩余信号res emd = EMD() emd.emd(Power_uni) imfs, res = emd.get_imfs_and_residue() N = len(imfs) P_H = np.sum(imfs[0:6,:],axis=0) P_M = np.sum(imfs[6:12,:],axis=0) P_L = res P_H =np.expand_dims(P_H,axis=1) P_M =np.expand_dims(P_M,axis=1) P_L =np.expand_dims(P_L,axis=1) Nwp = Data00[:,0:7] Nwp_train = Nwp[0:96] N_min = np.min(Nwp_train,axis=0) N_gap = np.max(Nwp_train,axis=0)-np.min(Nwp_train,axis=0) Nwp_uni = (Nwp-N_min)/N_gap#(N,7) Weather = Data00[:,7:13] Weather_train = Weather[0:96] W_min = np.min(Weather_train,axis=0) W_gap = np.max(Weather_train,axis=0)-np.min(Weather_train,axis=0) Weather_uni = (Weather-W_min)/W_gap#(N,6) 优化代码

Python数据分析入门：NumPy基础与数据处理流程

- shape：返回数组的形状，如data.shape返回(2, 3)，表示2行3列。 3. **创建特殊数组**： - 全零数组：data = np.zeros(10) 或二维全零数组：data = np.zeros((2, 5)) - 全一数组：data = np.ones(10...

Python Numpy：入门与数组操作详解

- np.ones(shape, dtype=None, order='C')用于创建全为1的数组，shape定义数组的形状，dtype指定数据类型，order通常设为'C'，代表C风格内存布局。 4. 多维数组操作: Numpy的核心优势在于其多维数组处理...

import numpy as np import pickle import math f = open(r'C:\Users\sdnugeo\Desktop\target2.pkl','rb') bi = pickle.load(f) # bi = np.array(bi) data = np.arange(0, 156) print(data) data = data.tolist() # print(data) # 每次抽取1000个数值，共抽取10次 samples = [] c = [] a = 11 r = math.ceil(len(data)/a) print(r) for i in range(a): if len(data) > r : sample = np.random.choice(data, r, replace=False) else: sample = np.random.choice(data, len(data), replace=False) # for s in sample: # data.remove(s) # continue # print(type(sample)) # sample2 = np.array(sample) b = [bi[j] for j in list(sample)] # d = np.array(b) print(type(b)) c.append(b) # c = np.concatenate(c, axis=0) # c = bi[0] print(sample) # print(b) samples.append(sample) for s in sample: data.remove(s) # sample = [s for s in sample if s in data] samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) c = [np.array(e) for e in c] c = np.concatenate(c, axis=0) # samples = np.array(samples).reshape(-1) print(samples) print(c.shape) 请帮我把这段代码修改成可调用的函数

data = data.tolist() samples = [] c = [] a = 11 r = math.ceil(len(data)/a) for i in range(a): if len(data) > r: sample = np.random.choice(data, r, replace=False) else: sample = np.random....

请解释以下代码： data = np.loadtxt('data/{}.txt'.format(dataset)) n, _ = data.shape idx = np.array([i for i in range(n)], dtype=np.int32) idx_map = {j: i for i, j in enumerate(idx)} edges_unordered = np.genfromtxt(path, dtype=np.int32) edges = np.array(list(map(idx_map.get, edges_unordered.flatten())), dtype=np.int32).reshape(edges_unordered.shape) adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(n, n), dtype=np.float32)

2. n, _ = data.shape：获取 data 数组的形状，n 为数组的行数，表示节点的数量。 3. idx = np.array([i for i in range(n)], dtype=np.int32)：生成一个长度为 n 的一维数组 idx，其中每个元素代表一...

import pandas as pd import numpy as np from sklearn.cluster import KMeans import matplotlib.pyplot as plt def data_input(): data=pd.read_excel('22AI1.xlsx') data=data.dropna(axis=0) data = data.reset_index(drop=True) X=data.身高 Y=data.体重 X=np.array(X).reshape(-1,1) Y=np.array(Y).reshape(-1,1) return X,Y X,Y=data_input()[0],data_input()[1] X_trian=np.concatenate((X,Y),axis=1) clf=KMeans(n_clusters=3) clf.fit(X_trian) print(clf.labels_) plt.scatter(X,Y,c=clf.labels_) plt.show() ,报错ValueError: c of shape (35,) not acceptable as a color sequence for x with size 38, y with size 38

X=np.array(X).reshape(-1,1) Y=np.array(Y).reshape(-1,1) return X,Y X,Y=data_input()[0],data_input()[1] X_train=np.concatenate((X,Y),axis=1) clf=KMeans(n_clusters=3) clf.fit(X_train) ...

import numpy as np import pickle f = open(r'C:\Users\sdnugeo\Desktop\target2.pkl','rb') bi = pickle.load(f) # bi = np.array(bi) data = np.arange(0, 156) print(data) data = data.tolist() # print(data) # 每次抽取1000个数值，共抽取10次 samples = [] c = [] for i in range(11): if len(data) > 15: sample = np.random.choice(data, 15, replace=False) else: sample = np.random.choice(data, 6, replace=False) # continue # print(type(sample)) # sample2 = np.array(sample) b = [bi[j] for j in list(sample)] # d = np.array(b) print(type(b)) c.append(b) # c = bi[0] print(sample) print(b) samples.append(sample) # sample = [s for s in sample if s in data] for s in sample: data.remove(s) samples = np.array(samples).reshape(-1) print(samples) print(data)，报错setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (11,) + inhomogeneous part.

samples = [np.array(s) for s in samples] samples = np.concatenate(samples, axis=0) 这样就可以将 samples 转换成一维的 NumPy 数组。另外，你在循环中的最后一次循环中没有把 sample 中的元素移除 ...

np.array.shape

np.array.shape是一个返回数组维度的属性，它返回一个元组，包含各个维度的大小。举个例子，对于一个一维数组p1，其shape为(3, )，表示这个数组有3个元素。对于一个二维数组p2，其shape为(2, 1)，表示这个数组有2行1...

def Predict(self, img): """ get class mask of image """ h_ori, w_ori = img.shape[:2] input_size = self.net.input_info["image"].input_data.shape h_resize, w_resize = input_size[-2:] img_pil = Image.fromarray(img) img_resize = img_pil.resize( (w_resize, h_resize), resample=BICUBIC) img_np = np.asarray(img_resize) / 255 # normalize # model input [1, 1, h, w] img_np = np.expand_dims(np.expand_dims(img_np, axis=0), axis=0) input = {'image': img_np} res = self.net.infer(inputs=input) output = res["mask"].squeeze(0) probs = softmax(output) mask = Image.fromarray(np.argmax(probs, axis=0).astype(np.uint8)) mask = mask.resize((w_ori, h_ori), resample=NEAREST) mask_np = np.asarray(mask) return mask_np

这段代码实现了一个神经网络模型对输入的图片进行分割（Semantic ...最后，将归类后的结果进行缩放以符合原始图片的大小（h_ori和w_ori），并将其转换为 numpy 数组的形式，作为该方法的返回值（mask_np）。

# 聚类数据 import numpy as np pos = np.arange(0, 6, 5) # 两个中心点在x=0，x=5 size = 50 # 生成两个簇 x = np.array([ np.random.normal(i+np.random.random(), 1, size) for i in pos ]).flatten() y = np.array([ np.random.normal(i+np.random.random(), 1, size) for i in pos ]).flatten() print(x.shape, y.shape)编写Python程序，实现以下要求：画出上面数据的散点图采用scipy.cluster.vq.kmeans()聚类，并在散点图中画出聚类中心点

x = np.array([np.random.normal(i+np.random.random(), 1, size) for i in pos]).flatten() y = np.array([np.random.normal(i+np.random.random(), 1, size) for i in pos]).flatten() # 画出散点图 plt.scatter...

data, label = np.stack(data), np.array(label) idx = np.random.permutation(data.shape[0]) data, label = data[idx], label[idx] train_X, test_X, train_Y, test_Y = train_test_split(data, label, test_size=0.1)

接着，idx = np.random.permutation(data.shape[0])生成一个长度为数据集大小的随机排列数组，用于对数据和标签进行随机化处理。然后，data, label = data[idx], label[idx]将数据和标签按照随机排列数组的...

import numpy as np import matplotlib.pyplot as plt # 生成sin函数数据 x = np.arange(0, 2np.pi, 0.1) y = np.sin(x) # 可视化sin函数 plt.plot(x, y) plt.show() from keras.models import Sequential from keras.layers import Dense, SimpleRNN # 准备数据 dataX, dataY = [], [] for i in range(len(y)-1): dataX.append(y[i:i+1]) dataY.append(y[i+1]) dataX = np.array(dataX) dataY = np.array(dataY) # 划分训练集和测试集 train_size = int(len(dataY) 0.7) test_size = len(dataY) - train_size trainX, testX = np.array(dataX[0:train_size]), np.array(dataX[train_size:len(dataX)]) trainY, testY = np.array(dataY[0:train_size]), np.array(dataY[train_size:len(dataY)]) # 调整输入数据的形状 trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) # 定义模型结构 model = Sequential() model.add(SimpleRNN(units=10, input_shape=(1, 1))) model.add(Dense(units=1)) # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(trainX, trainY, epochs=10, validation_data=(testX, testY))梯度可视化

1. 导入TensorFlow和Keras的相关库：import tensorflow as tf from tensorflow import keras 2. 创建一个TensorBoard回调函数：tensorboard_callback = keras.callbacks.TensorBoard(log_dir=log_dir) 3. 将回调...

scaler = StandardScaler() data = scaler.fit_transform(data_trainnn) label = list(label_trainnn.values()) label = np.array(label).astype('category')报错 label = np.array(label).astype('category') ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (5,) + inhomogeneous part.

如果是这样，你需要将 label_trainnn.values() 转换为一个一维数组或者一个列表，以便将其作为 np.array() 的输入。你可以尝试使用 np.ravel() 函数或者 list() 函数对 label_trainnn.values() 进行转换，...

t1 =[] # 收货总量 t2 =[] # 日均收货量 t3 =[] # 单日最大收货量 t4 =[] # 日收货量极差 @ for i in range(city.shape[0]): if data[data[:,2]==city[i]].shape[0]!=0: t1+= [np.sum(data[data[:,2]==city[i]][:,-1])] t2+= [np.mean(data[data[:,2]==city[i]][:,-1])] t3+= [np.max(data[data[:,2]==city[i]][:,-1])] t4+= [np.ptp(data[data[:,2]==city[i]][:,-1])] else: t1+= [0] t2+= [0] t3+= [0] t4+= [0] t1 = np.array(t1)[:,None] t2 = np.array(t2)[:,None] t3 = np.array(t3)[:,None] t4 = np.array(t4)[:,None]

如果存在，则通过筛选条件data[:,2]==city[i]来获取该城市的所有收货数据。 2. 如果存在收货数据，则分别计算该城市的收货总量、日均收货量、单日最大收货量和日收货量极差，并将结果添加到对应的列表中。 3. 如果...

程序运行提示ValueError: could not broadcast input array from shape (921600,) into shape (307200,)，优化程序 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T # depth[depth==65535]=0 self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1) data_ply[1] = -self.Y.T.reshape(-1) data_ply[2] = -self.Z.T.reshape(-1) img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1) data_ply[4] = img[:, :, 1:2].reshape(-1) data_ply[5] = img[:, :, 2:3].reshape(-1) self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)

具体来说，self.X.T.reshape(-1)、-self.Y.T.reshape(-1) 和 -self.Z.T.reshape(-1) 这三行代码中，将二维数组转换成了一维数组，并且数组长度发生了变化。其中，self.X、self.Y 和 self.Z 都是形状为 ...

程序提示AttributeError: 'NoneType' object has no attribute 'shape'，优化程序def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T # depth[depth==65535]=0 self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1) data_ply[1] = -self.Y.T.reshape(-1) data_ply[2] = -self.Z.T.reshape(-1) img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1) data_ply[4] = img[:, :, 1:2].reshape(-1) data_ply[5] = img[:, :, 2:3].reshape(-1) self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)

这个错误通常是由于...print('Shape of X:', X.shape) print('Shape of Y:', Y.shape) 这样可以方便地查看 X 和 Y 是否有正确的形状。如果仍然无法解决问题，建议提供更多代码和错误信息，以便更好地排查问题。

for i in range(1, dimensions + 1): filename = 'AtrialFibrillationDimension' + str(i) + '_TEST.arff' # change to '_TRAIN.arff' to build training data file = open(filename, "r") dataset = arff.load(file) dataset = np.array(dataset['data']) data.append(dataset[ : , 0 : -1]) data = np.array(data) data = np.transpose(data, (1, 2, 0)) print(data.shape) np.save('X_test.npy', data)

最后，它打印出data的形状，并使用np.save函数将data保存为名为'X_test.npy'的文件。这段代码的作用是加载ARFF文件中的多维时间序列数据，并将其保存为numpy数组，以便进行后续的数据处理和分析。

X = np.array(data[:-1]).reshape(-1, data.shape[1] * data.shape[2]) y = np.array(data[-1]).ravel()

那么首先通过 data[:-1] 取出前面 m-1 个样本，然后通过 reshape 将这些样本展平成二维数组，形状为 (m-1, n * p)。这里使用了 -1 表示自动计算该维度的大小。接着，通过 data[-1] 取出最后一个样本，...

解释这段代码dataX, dataY = [], [] for i in range(len(y)-1): dataX.append(y[i:i+1]) dataY.append(y[i+1]) dataX = np.array(dataX) dataY = np.array(dataY) # 划分训练集和测试集 train_size = int(len(dataY) * 0.7) test_size = len(dataY) - train_size trainX, testX = np.array(dataX[0:train_size]), np.array(dataX[train_size:len(dataX)]) trainY, testY = np.array(dataY[0:train_size]), np.array(dataY[train_size:len(dataY)]) # 调整输入数据的形状 trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) # 定义模型结构 model = Sequential() model.add(SimpleRNN(units=10, input_shape=(1, 1))) model.add(Dense(units=1)) # 编译模型 model.compile(optimizer='adam', loss='mse') # 训练模型 history = model.fit(trainX, trainY, epochs=10, validation_data=(testX, testY))

1.将原始数据y拆分为输入数据dataX和输出数据dataY，其中dataX的每个元素为y中的一个元素，dataY的每个元素为对应dataX元素的下一个元素。 2.将dataX和dataY转换为numpy数组格式，并将其划分为训练集和测试集。 3....

import numpy as np import pandas as pd from keras.models import Sequential from keras.layers import Dense, LSTM # 读取csv文件 data = pd.read_csv('3c_left_1-6.csv') # 将数据转换为numpy数组 data = np.array(data) data = data.reshape((data.shape[0], 1, data.shape[1])) # 获取数据的维度信息 n_samples, n_timesteps, n_features = data.shape # 定义模型 model = Sequential() model.add(LSTM(64, input_shape=(n_timesteps, n_features), return_sequences=True)) model.add(Dense(n_features)) # 编译模型 model.compile(loss='mse', optimizer='adam') # 训练模型 model.fit(data, data, epochs=1, batch_size=32) # 对数据进行去噪 denoised_data = model.predict(data) # 计算去噪后的SNR,MSE,PSNR snr = np.mean(np.power(data, 2)) / np.mean(np.power(data - denoised_data, 2)) mse = np.mean(np.power(data - denoised_data, 2)) psnr = 10 * np.log10((np.power(data.max(), 2) / mse)) print("Signal-to-Noise Ratio (SNR): {:.2f} dB".format(snr)) print("Mean Squared Error (MSE): {:.2f}".format(mse)) print("Peak Signal-to-Noise Ratio (PSNR): {:.2f} dB".format(psnr)) data = {'SNR': [snr], 'MSE': [mse], 'PSNR': [psnr]} df = pd.DataFrame(data) df.to_csv('indicator_lstm.csv', index=False) # 将结果保存为csv文件 denoised_data = pd.DataFrame(denoised_data.reshape(n_samples, n_timesteps * n_features)) denoised_data.to_csv('denoised_data_lstm.csv', index=False)

2. 定义 LSTM 模型，其中输入的数据维度为 (n_timesteps, n_features)，输出的数据维度也为 (n_timesteps, n_features)。 3. 编译模型，定义损失函数和优化器。 4. 训练模型，使用相同的输入和输出数据进行训练。 5....

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data, label = np.stack(data), np.array(label) idx = np.random.permutation(data.shape[0]) data, label = data[idx], label[idx] train_X, test_X, train_Y, test_Y = train_test_split(data, label, test_size=0.1)

X = np.array(data[:-1]).reshape(-1, data.shape[1] * data.shape[2]) y = np.array(data[-1]).ravel()

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