init_action_G = np.hstack((np.real(self.G.reshape(1, -1)), np.imag(self.G.reshape(1, -1))))

程序执行提示AttributeError: 'point_cloud_generator' object has no attribute 'widthself'，优化程序class point_cloud_generator(): def init(self, rgb_file, depth_file, save_ply, camera_intrinsics=[784.0, 779.0, 649.0, 405.0]): self.rgb_file = rgb_file self.depth_file = depth_file self.save_ply = save_ply self.rgb = cv2.imread(rgb_file) self.depth = cv2.imread(self.depth_file, -1) print("your depth image shape is:", self.depth.shape) self.width = self.rgb.shape[1] self.height = self.rgb.shape[0] self.camera_intrinsics = camera_intrinsics self.depth_scale = 1000 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)[:self.widthself.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.widthself.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.widthself.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.widthself.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.widthself.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.widthself.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)

data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1]....

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)

if self.depth is None or self.camera_intrinsics is None or self.rgb is None: # 输入数据为空，直接返回或抛出异常 return if self.depth.size == 0: # depth为空数组，直接返回或抛出异常 return # ... ...

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 ...

没有GPU，优化程序class point_cloud_generator(): def init(self, rgb_file, depth_file, save_ply, camera_intrinsics=[312.486, 243.928, 382.363, 382.363]): self.rgb_file = rgb_file self.depth_file = depth_file self.save_ply = save_ply self.rgb = cv2.imread(rgb_file) self.depth = cv2.imread(self.depth_file, -1) print("your depth image shape is:", self.depth.shape) self.width = self.rgb.shape[1] self.height = self.rgb.shape[0] self.camera_intrinsics = camera_intrinsics self.depth_scale = 1000 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T 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)[:self.width * self.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.width * self.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.width * self.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.width * self.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1) def write_ply(self): start = time.time() float_formatter = lambda x: "%.4f" % x points = [] for i in self.data_ply

1. Import the numba library by adding the following line at the top of your code: python import numba 2. Add the @numba.jit(nopython=True) decorator to the compute method to enable ...

my_list1=input(np.array('my_list1=')) my_list2=input(np.array('my_list2=')) print(my_list2) print(my_list1) A =np.mat(my_list1) B=np.mat(my_list2) 矩阵输出只有一行怎么办

A = A.reshape((-1, 1)) # 转换为n x 1的矩阵 B = B.reshape((-1, 1)) # 转换为n x 1的矩阵其中，-1表示自动计算该维度的大小，以保证矩阵中的元素数量不变。这样，您就可以将原本只有一行的矩阵转换为列...

elif count == 7: x_train_one = np.reshape(x_train[:, :, 0], [len(x_train), cold, 1]) x_train_two = np.reshape(x_train[:, :, 1], [len(x_train), cold, 1]) x_train_three = np.reshape(x_train[:, :, 2], [len(x_train), cold, 1]) x_train_four = np.reshape(x_train[:, :, 3], [len(x_train), cold, 1]) x_train_five = np.reshape(x_train[:, :, 4], [len(x_train), cold, 1]) x_train_six = np.reshape(x_train[:, :, 5], [len(x_train), cold, 1]) x_train_seven = np.reshape(x_train[:, :, 6], [len(x_train), cold, 1]) x_test_one = np.reshape(x_test[:, :, 0], [len(x_test), cold, 1]) x_test_two = np.reshape(x_test[:, :, 1], [len(x_test), cold, 1]) x_test_three = np.reshape(x_test[:, :, 2], [len(x_test), cold, 1]) x_test_four = np.reshape(x_test[:, :, 3], [len(x_test), cold, 1]) x_test_five = np.reshape(x_test[:, :, 4], [len(x_test), cold, 1]) x_test_six = np.reshape(x_test[:, :, 5], [len(x_test), cold, 1]) x_test_seven = np.reshape(x_test[:, :, 6], [len(x_test), cold, 1]) input_out = [x_train_one, x_train_two, x_train_three, x_train_four, x_train_five, x_train_six, x_train_seven] layer_out = [x_test_one, x_test_two, x_test_three, x_test_four, x_test_five, x_test_six, x_test_seven] return input_out, layer_out

首先，对于训练集数据x_train，它将第一维度的数据（即[:, :, 0]）进行重塑，使得每个样本的维度变为[cold, 1]，然后分别赋值给x_train_one、x_train_two、x_train_three等变量。同样的操作也对测试集数据x...

class TwoLayerNet: def init(self, input_size, hidden_size1,hidden_size2, output_size, weight_init_std=0.01): # 初始化权重 self.params = {} self.params['W1'] = weight_init_std * \ np.random.randn(input_size, hidden_size1) self.params['b1'] = np.zeros(hidden_size1) self.params['W2'] = weight_init_std * \ np.random.randn(hidden_size1, hidden_size2) self.params['b2'] = np.zeros(hidden_size2) self.params['W3'] = weight_init_std * \ np.random.randn(hidden_size2, output_size) self.params['b3'] = np.zeros(output_size) self.params['ConVW1']=0.4np.random.normal(0,1,size=150).reshape(6,5,5,1).transpose(0, 3, 1, 2) self.params['ConVW2']=0.25np.random.normal(0,1,size=2400).reshape(16,6,5,5) self.params['ConVB1']=np.zeros(6) self.params['ConVB2']=np.zeros(16)

这段代码定义了一个名为TwoLayerNet的类，用于构建一个具有两个隐藏层的神经网络模型。...卷积权重ConVW1和ConVW2也是通过随机生成服从高斯分布的数值来初始化的，而偏置ConVB1和ConVB2则被初始化为全零。

def load_dataset(seq_len,batch_size=32): note_arr = np.load("notes_array.npy") _n_notes, _n_durations = note_arr.shape[1:] offset_arr = np.load("offsets_array.npy") _n_offsets = offset_arr.shape[1] note_arr = np.reshape(note_arr, (note_arr.shape[0], -1)) note_data = np.concatenate([note_arr, offset_arr], axis=-1) _n_embeddings = note_data.shape[-1]

接着，通过np.reshape()函数将note_arr数组转换成二维矩阵形式，方便后续处理。最后，该函数返回了经过预处理和划分后的数据集dataset，以及音符、节奏和偏移量的个数n_notes、n_durations和n_offsets，以及嵌入向量...

class Logistic_Regression: def init(self, lr_rate = 0.01, max_iter = 100000, tol = 1e-2 ): self.lr_rate = lr_rate self.max_iter = max_iter self.tol = tol self.w = None def preprocessing(self, x): row = x.shape[0] y = np.ones(row).reshape(row, 1) x_prepro = np.hstack((x, y)) return x_prepro def sigmoid(self, x): return 1 / (1 + np.exp(-x)) def fit(self, x_train, y_train): X = self.preprocessing(x_train) y = y_train.T self.w = np.array([[0] * X.shape[1]], dtype = float) k = 0 for loop in range(self.max_iter): z = np.dot(X, self.w.T) grad = X * (y - self.sigmoid(z)) grad = grad.sum(axis = 0) if (np.abs(grad) < self.tol).all(): break else: self.w += self.lr_rate * grad k += 1 print("最终的迭代次数为：".format(k)) print("最终的梯度为：".format(grad)) print("最终的权重为：".format(self.w[0])) return self.w[0] def predict(self, x): p = self.sigmoid(np.dot(self.preprocessing(x), self.w.T )) print("Y = 1的概率被估计为：{}".format(p[0][0])) p[np.where(p > 0.5)] = 1 p[np.where(p < 0.5)] = 0 return p def score(self, x, y): y_c = self.predict(x) error_rate = np.sum(np.abs(y_c - y.T)) / y_c.shape[0] return 1 - error_rate

其中，预处理方法将输入的x矩阵增加一列全为1的列，sigmoid函数是逻辑回归中的激活函数，拟合方法使用梯度下降法来更新权重，预测方法将输入的x矩阵预处理后，使用训练好的权重来计算Y=1的概率，并将概率大于0.5的...

运行提示numba.core.errors.TypingError: Failed in nopython mode pipeline (step: nopython frontend)，优化程序@numba.jit(nopython=True) def compute(self): # rest of the code t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T 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 numba.prange(self.width): # rest of the code X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in numba.prange(self.height): # rest of the code 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)[:self.width * self.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.width * self.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.width * self.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.width * self.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1)

这个错误提示是因为 Numba 在尝试编译...print(type(self.depth)) print(self.depth.shape) 这将输出 self.depth 的类型和形状。你可以在代码中添加类似的语句，以查看变量的类型和形状，从而更好地调试代码。

NameError Traceback (most recent call last) Cell In[1], line 1 ----> 1 x_grid, y_grid = np.meshgrid(np.linspace(-4, 4, 100), np.linspace(-4, 4, 100)) 2 xy_grid = np.hstack([x_grid.reshape(-1,1), y_grid.reshape(-1,1)]) 3 xy_grid_tensor = torch.from_numpy(xy_grid.astype(np.float32)) NameError: name 'np' is not defined

这个错误提示显示 np 没有定义，因此你需要在代码的开头添加以下导入语句： python import numpy as np 这将导入NumPy库并将其命名为np，使得你的代码中可以使用NumPy中的函数和方法。

class Pooling: def init(self, pool_h, pool_w, stride=1, pad=0): self.pool_h = pool_h self.pool_w = pool_w self.stride = stride self.pad = pad self.x = None self.arg_max = None def forward(self, x): N, C, H, W = x.shape out_h = int(1 + (H - self.pool_h) / self.stride) out_w = int(1 + (W - self.pool_w) / self.stride) col = im2col(x, self.pool_h, self.pool_w, self.stride, self.pad) col = col.reshape(-1, self.pool_hself.pool_w) arg_max = np.argmax(col, axis=1) out = np.max(col, axis=1) out = out.reshape(N, out_h, out_w, C).transpose(0, 3, 1, 2) self.x = x self.arg_max = arg_max return out def backward(self, dout): dout = dout.transpose(0, 2, 3, 1) pool_size = self.pool_h self.pool_w dmax = np.zeros((dout.size, pool_size)) dmax[np.arange(self.arg_max.size), self.arg_max.flatten()] = dout.flatten() dmax = dmax.reshape(dout.shape + (pool_size,)) dcol = dmax.reshape(dmax.shape[0] * dmax.shape[1] * dmax.shape[2], -1) dx = col2im(dcol, self.x.shape, self.pool_h, self.pool_w, self.stride, self.pad) return dx

这段代码实现了一个池化层的类，包括构造函数和前向传播函数、反向传播函数。构造函数用于初始化池化层的超参数，包括池化窗口的高度和宽度、步长、填充等，并初始化一些变量用于后续的前向计算和反向传播。...

import numpy as np class BPNeuralNetwork: def init(self, input_size, hidden_size, output_size): self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size # 初始化权重和偏置 self.weights_ih = np.random.randn(hidden_size, input_size) self.bias_ih = np.random.randn(hidden_size, 1) self.weights_ho = np.random.randn(output_size, hidden_size) self.bias_ho = np.random.randn(output_size, 1) # 定义激活函数 self.activation = lambda x: 1 / (1 + np.exp(-x)) self.derivative = lambda x: x * (1 - x) def forward(self, inputs): # 计算隐藏层的输出 hidden = self.activation(np.dot(self.weights_ih, inputs) + self.bias_ih) # 计算输出层的输出 output = self.activation(np.dot(self.weights_ho, hidden) + self.bias_ho) return output def backward(self, inputs, targets, output): # 计算输出层的误差 output_error = targets - output output_delta = output_error * self.derivative(output) # 计算隐藏层的误差 hidden_error = np.dot(self.weights_ho.T, output_delta) hidden_delta = hidden_error * self.derivative(hidden) # 更新权重和偏置 self.weights_ho += np.dot(output_delta, hidden.T) self.bias_ho += output_delta self.weights_ih += np.dot(hidden_delta, inputs.T) self.bias_ih += hidden_delta def train(self, inputs, targets, epochs): for i in range(epochs): for j in range(len(inputs)): # 前向传播 output = self.forward(inputs[j].reshape(-1, 1)) # 反向传播 self.backward(inputs[j].reshape(-1, 1), targets[j].reshape(-1, 1), output)

1. 初始化函数__init__中，定义了输入层、隐藏层和输出层的节点数，以及它们之间的权重和偏置。其中，权重和偏置都是随机初始化的。 2. 定义了激活函数activation和导数函数derivative，这里采用的是Sigmoid函数。 ...

datawin1, datawin2, datawin3, Currentout, Timeout = [], [], [], [], [] for i in range(look_back, len(dataset_feature)-look_back, 1): window = dataset_feature[i-look_back:i, 0:-1] window = np.reshape(window, (1, 6look_back)) window = np.array(window) datawin1.append(window) datawin2.append(dataY[i-1]) Timeout.append(Time[i-1]) Currentout.append(Current[i-1]) datawin1 = np.array(datawin1) datawin2 = np.array(datawin2) datawin2 = np.reshape(datawin2, (datawin2.shape[0], datawin2.shape[1])) datawin1 = np.reshape(datawin1, (datawin1.shape[0], 6look_back)) Timeout = np.array(Timeout) Currentout = np.array(Currentout) Timeout = np.reshape(Timeout, (Timeout.shape[0], 1)) Currentout = np.reshape(Currentout, (Currentout.shape[0], 1)) dataset_featureout = np.concatenate((datawin1, datawin2), axis=1)

这段代码看起来是在进行数据窗口化的操作，将原始数据集的某个时间段内的数据作为一个窗口，然后将...整个操作中，look_back 参数用于指定时间窗口的长度，np.reshape 用于将数据转换为正确的形状以便后续处理。

class GP: def init(self, num_x_samples): self.observations = {"x": list(), "y": list()} self.num_x_samples = num_x_samples self.x_samples = np.arange(0, 10.0, 10.0 / self.num_x_samples).reshape(-1, 1) # prior self.mu = np.zeros_like(self.x_samples) self.cov = self.kernel(self.x_samples, self.x_samples) def update(self, observations): self.update_observation(observations) x = np.array(self.observations["x"]).reshape(-1, 1) y = np.array(self.observations["y"]).reshape(-1, 1) K11 = self.cov # (N,N) K22 = self.kernel(x, x) # (k,k) K12 = self.kernel(self.x_samples, x) # (N,k) K21 = self.kernel(x, self.x_samples) # (k,N) K22_inv = np.linalg.inv(K22 + 1e-8 * np.eye(len(x))) # (k,k) self.mu = K12.dot(K22_inv).dot(y) self.cov = self.kernel(self.x_samples, self.x_samples) - K12.dot(K22_inv).dot(K21) gp = GP(num_x_samples=100)解释一下gp = GP(num_x_samples=100)

这是一个名为GP的类，它有一个初始化函数__init__，需要传入num_x_samples参数。它有两个成员变量observations和num_x_samples，observations是一个字典，包含"x"和"y"两个键，分别对应一个空列表，用于存储观测数据...

init_action_G = np.hstack((np.real(self.G.reshape(1, -1)), np.imag(self.G.reshape(1, -1))))

init_action_G = np.hstack((np.real(self.G.reshape(1, -1)), np.imag(self.G.reshape(1, -1))))这行代码生成的矩阵维数

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init_action_G = np.hstack((np.real(self.G.reshape(1, -1)), np.imag(self.G.reshape(1, -1))))

init_action_G = np.hstack((np.real(self.G.reshape(1, -1)), np.imag(self.G.reshape(1, -1))))这行代码生成的矩阵维数

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my_list1=input(np.array('my_list1=')) my_list2=input(np.array('my_list2=')) print(my_list2) print(my_list1) A =np.mat(my_list1) B=np.mat(my_list2) 矩阵输出只有一行怎么办

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