IndexError Traceback (most recent call last) Cell In[13], line 3 1 new_array=np.zeros((4,12,100,9)) 2 for i in range(len(df_array)): ----> 3 new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]]+=df_array[i][3] 4 df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) 5 i=0 IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices该如何解决？

Python自学教程-12-了解异常类型.ev4.rar

1. ZeroDivisionError：当尝试除以零时引发。例如，5 / 0会导致这个异常。 2. FileNotFoundError：当尝试打开不存在的文件时抛出。 3. TypeError：在操作或函数应用于不适当类型的对象时抛出。例如，将字符...

1 new_array=np.zeros((4,12,100,9)) 2 for i in range(len(df_array)): ----> 3 new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]]+=df_array[i][3] 4 df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) 5 i=0 IndexError: index -1953 is out of bounds for axis 0 with size 4需要如何修改

if df_array[i][5] < 2013 or df_array[i][5] > 2016 or df_array[i][6] < 1 or df_array[i][6] > 12 or df_array[i][1] < 0 or df_array[i][1] > 99 or df_array[i][2] < 0 or df_array[i][2] > 8: continue ...

帮我看看这段代码报错原因：Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 272, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 48, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/losses.py", line 37, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index. 部分代码如下：cls_num_list = np.zeros(classes) for _, label in train_loader.dataset: cls_num_list[label] += 1 criterion_train = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, s=30) class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.s*output, target, weight=self.weight)

根据报错信息，可以看出问题出在这一行代码上：index.scatter_(1, target.data.view(-1, 1), 1)，报错信息中指出 scatter_() 函数期望得到的是 int64 类型的索引，但是目前传入的 target.data.view(-1, 1) ...

new_array=np.zeros((4,12,100,9)) 2 for i in range(len(df_array)): ----> 3 new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]]+=df_array[i][3] 4 df_new = pd.DataFrame(columns=['创建年', '创建月', '物料编号', '应发库', '销售订单数量']) IndexError: only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices需要如何更改

new_array = np.zeros((4, 12, 100, 9)) for i in range(array_len): new_array[df_array[i][5]-2013][df_array[i][6]-1][df_array[i][1]][df_array[i][2]] += df_array[i][3] df_new = pd.DataFrame(columns=['...

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, LSTM from sklearn.metrics import r2_score,median_absolute_error,mean_absolute_error # 读取数据 data = pd.read_csv(r'C:/Users/Ljimmy/Desktop/yyqc/peijian/销量数据rnn.csv') # 取出特征参数 X = data.iloc[:,2:].values # 数据归一化 scaler = MinMaxScaler(feature_range=(0, 1)) X[:, 0] = scaler.fit_transform(X[:, 0].reshape(-1, 1)).flatten() #X = scaler.fit_transform(X) #scaler.fit(X) #X = scaler.transform(X) # 划分训练集和测试集 train_size = int(len(X) * 0.8) test_size = len(X) - train_size train, test = X[0:train_size, :], X[train_size:len(X), :] # 转换为监督学习问题 def create_dataset(dataset, look_back=1): X, Y = [], [] for i in range(len(dataset) - look_back - 1): a = dataset[i:(i + look_back), :] X.append(a) Y.append(dataset[i + look_back, 0]) return np.array(X), np.array(Y) look_back = 12 X_train, Y_train = create_dataset(train, look_back) #Y_train = train[:, 2:] # 取第三列及以后的数据 X_test, Y_test = create_dataset(test, look_back) #Y_test = test[:, 2:] # 取第三列及以后的数据 # 转换为3D张量 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) # 构建LSTM模型 model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1))) model.add(LSTM(units=50)) model.add(Dense(units=1)) model.compile(loss='mean_squared_error', optimizer='adam') model.fit(X_train, Y_train, epochs=5, batch_size=32) #model.fit(X_train, Y_train.reshape(Y_train.shape[0], 1), epochs=10, batch_size=32) # 预测下一个月的销量 last_month_sales = data.tail(12).iloc[:,2:].values #last_month_sales = data.tail(1)[:,2:].values last_month_sales = scaler.transform(last_month_sales) last_month_sales = np.reshape(last_month_sales, (1, look_back, 1)) next_month_sales = model.predict(last_month_sales) next_month_sales = scaler.inverse_transform(next_month_sales) print('Next month sales: %.0f' % next_month_sales[0][0]) # 计算RMSE误差 rmse = np.sqrt(np.mean((next_month_sales - last_month_sales) ** 2)) print('Test RMSE: %.3f' % rmse)IndexError Traceback (most recent call last) Cell In[1], line 36 33 X_test, Y_test = create_dataset(test, look_back) 34 #Y_test = test[:, 2:] # 取第三列及以后的数据 35 # 转换为3D张量 ---> 36 X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1)) 37 X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1)) 38 # 构建LSTM模型 IndexError: tuple index out of range

具体地说，X_train的维度为 (样本数量, 时间步长, 特征数)，而在代码中reshape时将其调整为了 (样本数量, 时间步长, 1)，这表示只有一个特征，但实际上在读取数据时有多个特征。可能的解决方法是检查数据读取部分...

IndexError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_6300\2471113867.py in <module> 2 data=file_data['面积(㎡)'].values 3 for i in data: ----> 4 data_new=np.append(data_new,np.array(i[:-2])) 5 data=data_new.astype(np.float64) 6 file_data.loc[:,'面积(㎡)']=data IndexError: invalid index to scalar variable.

这个错误是因为你在使用np.array(i[:-2])的时候，i的类型为标量，而不是字符串或者数组。因此，你需要在将i转换为字符串之前，先检查一下它的类型，如果它不是字符串，你可以使用str()函数将其转换为字符串...

IndexError Traceback (most recent call last) <ipython-input-8-8dffcb90c735> in <module>() 26 return projected_data 27 start_time = time.time() ---> 28 X_lle = lle(X) 29 elapsed_time = time.time() - start_time 30 plt.figure() <ipython-input-8-8dffcb90c735> in lle(data, n_neighbors, n_components) 18 C += regularization_term * np.eye(n_neighbors) 19 W = np.linalg.solve(C,np.ones(n_neighbors)) ---> 20 W[i,indices[i]] = W / np.sum(W) 21 M = (np.eye(len(data)) - W).T @ (np.eye(len(data)) - W) 22 eigenvalues,eigenvectors = np.linalg.eigh(M) IndexError: too many indices for array: array is 1-dimensional, but 2 were indexed

这个错误提示是因为在第20行代码中，你尝试同时索引一个一维数组的两个元素，导致了索引错误。你需要检查一下W数组的形状，看看它是否是一个二维数组，如果是，那么你需要使用W[i][indices[i]]的形式来完成索引操作...

代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.soutput, target, weight=self.weight) 报错：Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 280, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 46, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/utils.py", line 182, in forward ldam_loss = self.ldam_loss(x, target) File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/utils.py", line 148, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index.

根据报错信息，你需要将 index 的数据类型转换为 int64。可以在 index 定义时指定它的数据类型为 int64： ...index.scatter_(1, target.data.view(-1, 1), 1) 这样应该就能解决这个错误了。

ndexError Traceback (most recent call last) ~\AppData\Local\Temp\ipykernel_6300\3642046433.py in <module> 2 data=file_data['面积(㎡)'].values 3 for i in data: ----> 4 data_new=np.append(data_new,np.array(i[:-2])) 5 data=data_new.astype(np.float64) 6 IndexError: invalid index to scalar variable.

这段代码似乎出现了一个错误，具体是在使用i[:-2]这个索引时出现了问题，因为i是一个标量而不是一个数组。...另外，你也可以在使用np.append函数之前初始化data_new数组，避免出现这种错误。

代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.int64) # uint8 index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.soutput, target, weight=self.weight) 报错：Traceback (most recent call last): File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 280, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/bder73002/hpy/ConvNextV2_Demo/train+.py", line 46, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/utils.py", line 183, in forward ldam_loss = self.ldam_loss(x, target) File "/home/bder73002/anaconda3/envs/python3.9.2/lib/python3.9/site-packages/torch/nn/modules/module.py", line 889, in _call_impl result = self.forward(*input, **kwargs) File "/home/bder73002/hpy/ConvNextV2_Demo/models/utils.py", line 149, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index.

根据报错信息，你需要将 index 的数据类型转换为 torch.int64。在代码中，index 的数据类型被初始化为 ...index = torch.zeros_like(x, dtype=self.index_dtype) 这样就可以避免 scatter_() 函数报错了。

def k_medoids(X, n_clusters, max_iter=100): # 初始化类簇中心点 medoids = random.sample(range(len(X)), n_clusters) labels = None for _ in range(max_iter): # 计算所有样本与中心点的距离 distances = pairwise_distances(X, X[medoids]) # 分配样本到最近的中心点 new_labels = np.argmin(distances, axis=1) # 更新中心点 for i in range(n_clusters): cluster_samples = np.where(new_labels == i)[0] new_medoid = np.argmin(np.sum(distances[cluster_samples][:, cluster_samples], axis=1)) medoids[i] = cluster_samples[new_medoid] # 判断是否收敛 if np.array_equal(labels, new_labels): break labels = new_labels return medoids, labels IndexError: index 2 is out of bounds for axis 1 with size 2

根据错误信息，这里的问题出在 np.argmin(distances[cluster_samples][:, cluster_samples], axis=1) 这一行。可能的原因是聚类的数量 n_clusters 大于样本数量 len(X)，导致 medoids 中的索引超出了距离矩阵的...

# 提取车牌部分图片 image = origin_image.copy() # 图像去噪灰度处理 gray_image = gray_guss(image) # 显示灰度图像 plt_show(gray_image) frame = cv2.resize(gray_image,(640,480)) outframe = hdmi_out.newframe() outframe = np.zeros((480, 640, 3), dtype=np.uint8) outframe[0:480,0:640,:] = frame[0:480,0:640,:] hdmi_out.writeframe(outframe)IndexError Traceback (most recent call last) <ipython-input-14-7bd7a92d396f> in <module> 8 outframe = hdmi_out.newframe() 9 outframe = np.zeros((480, 640, 3), dtype=np.uint8) ---> 10 outframe[0:480,0:640,:] = frame[0:480,0:640,:] 11 hdmi_out.writeframe(outframe) IndexError: too many indices for array: array is 2-dimensional, but 3 were indexed

这个错误提示是因为 outframe...outframe[:,:,1] = frame[:,:] outframe[:,:,2] = frame[:,:] hdmi_out.writeframe(outframe) 这样就可以将灰度图像 frame 复制到 outframe 的所有通道中，然后输出到 HDMI。

# 评估分类器 confMat = np.zeros((len(np.unique(testLabels)), len(np.unique(testLabels))), dtype=np.int32) for i in range(len(testLabels)): confMat[int(testLabels[i]), int(predictedLabels[i])] += 1 accuracy = np.trace(confMat) / np.sum(confMat)这段代码会出现：Traceback (most recent call last): File "D:\Crack_test\classifierOfSVM.py", line 94, in <module> confMat[int(testLabels[i]), int(predictedLabels[i])] += 1 IndexError: index 1 is out of bounds for axis 0 with size 1错误，请改正

这个错误的原因是 confMat 的第一维大小只有 1，而你的索引超出了范围。你需要检查一下 confMat 的大小是否正确。如果你使用的是多分类问题，那么你需要将 confMat 的大小设置为类别数目的平方。例如，如果...

IndexError Traceback (most recent call last) <ipython-input-23-2818760d9d6c> in <module>() 1 sca = (Scatter(init_opts=opts.InitOpts(theme=ThemeType.ESSOS,width='980px')) 2 .add_xaxis([i[1] for i in data_pair]) ----> 3 .add_yaxis('The Car Info',[[i[2],i[0]] for i in data_pair], 4 symbol_size=7, 5 label_opts=opts.LabelOpts(formatter=JsCode(label_js),position='right',distance=5), <ipython-input-23-2818760d9d6c> in (.0) 1 sca = (Scatter(init_opts=opts.InitOpts(theme=ThemeType.ESSOS,width='980px')) 2 .add_xaxis([i[1] for i in data_pair]) ----> 3 .add_yaxis('The Car Info',[[i[2],i[0]] for i in data_pair], 4 symbol_size=7, 5 label_opts=opts.LabelOpts(formatter=JsCode(label_js),position='right',distance=5), IndexError: list index out of range

1. data_pair 列表为空，导致 [[i[2],i[0]] for i in data_pair] 中的 i 无法访问到列表中的元素。你可以通过在使用 data_pair 前先检查它是否为空来解决这个问题。 2. data_pair 中的某些元素不是列表或...

def GM11(x0): x1 = np.cumsum(x0) z1 = (x1[:-1] + x1[1:]) / 2.0 B = np.append(-z1.reshape(-1, 1), np.ones_like(z1).reshape(-1, 1), axis=1) Y = x0[1:].reshape(-1, 1) [[a], [b]] = np.dot(np.dot(np.linalg.inv(np.dot(B.T, B)), B.T), Y) X = np.zeros_like(x0) X[0] = x0[0] for i in range(1, len(x0)): X[i] = (x0[0] - b/a) * np.exp(-a(i-1)) - (x0[0] - b/a) np.exp(-a*i) return X X0 = data['close'].values X1 = np.array([GM11(X0[i:i+5]) for i in range(len(X0)-4)]) s = np.zeros(len(X0)) s[0] = 1 for i in range(1, len(X0)): if X0[i] > X1[:, i-1].max(): s[i] = np.argmin(X1[:, i-1]) + 2 else: s[i] = np.argmin(X1[:, i-1]) + 1 --------------------------------------------------------------------------- IndexError Traceback (most recent call last) D:\.temp\ipykernel_10000\3121687314.py in <module> 5 s[0] = 1 6 for i in range(1, len(X0)): ----> 7 if X0[i] > X1[:, i-1].max(): 8 s[i] = np.argmin(X1[:, i-1]) + 2 9 else: IndexError: index 5 is out of bounds for axis 1 with size 5给出修正的代码

X1 = np.array([GM11(X0[i:i+5]) for i in range(len(X0)-4)]) s = np.zeros(len(X0)) s[0] = 1 for i in range(1, len(X0)-4): if X0[i+4] > X1[:, i].max(): s[i+4] = np.argmin(X1[:, i]) + 2 else: s[i+4] ...

Traceback (most recent call last): File "D:\pythonProject\main.py", line 157, in <module> for step, (x, y) in enumerate(train_loader): File "C:\ProgramData\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 634, in next data = self._next_data() File "C:\ProgramData\anaconda3\lib\site-packages\torch\utils\data\dataloader.py", line 678, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "C:\ProgramData\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py", line 51, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "C:\ProgramData\anaconda3\lib\site-packages\torch\utils\data\_utils\fetch.py", line 51, in data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\pythonProject\main.py", line 45, in getitem return self.getitem(idx + 1) File "D:\pythonProject\main.py", line 45, in getitem return self.getitem(idx + 1) File "D:\pythonProject\main.py", line 45, in getitem return self.getitem(idx + 1) [Previous line repeated 800 more times] File "D:\pythonProject\main.py", line 36, in getitem filename = os.path.basename(self.filenames[idx]) IndexError: list index out of range

在你的代码中，程序出现了一连串的递归调用，导致idx的值一直增加，直到超出了self.filenames列表的索引范围，从而引发了IndexError异常。建议检查你的代码，确保递归调用的正确性，检查self.filenames列表中是否...

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