numpy

问题 I have a 3d numpy array describing a polycube (imagine a 3d tetris piece). How can I calculate all 24 rotations? Numpy's array manipulation routines includes a rot90 method, which gives 4 of the 24, but I'm clueless how to calculate the rest. My only idea is to convert the 3d array to a 2d matrix of co-ordinates, multiply by a rotation matrix, and convert back. But I'd rather work directly with the 3d array. Example 2x2x2 array: >>> from numpy import array >>> polycube array([[[1, 0], [1, 0]]

问题 I have a an array that is relatively sparse, and I would like to go through each row and shuffle only the non-zero elements. Example Input: [2,3,1,0] [0,0,2,1] Example Output: [2,1,3,0] [0,0,1,2] Note how the zeros have not changed position. To shuffle all elements in each row (including zeros) I can do this: for i in range(len(X)): np.random.shuffle(X[i, :]) What I tried to do then is this: for i in range(len(X)): np.random.shuffle(X[i, np.nonzero(X[i, :])]) But it has no effect. I've

问题 I have a an array that is relatively sparse, and I would like to go through each row and shuffle only the non-zero elements. Example Input: [2,3,1,0] [0,0,2,1] Example Output: [2,1,3,0] [0,0,1,2] Note how the zeros have not changed position. To shuffle all elements in each row (including zeros) I can do this: for i in range(len(X)): np.random.shuffle(X[i, :]) What I tried to do then is this: for i in range(len(X)): np.random.shuffle(X[i, np.nonzero(X[i, :])]) But it has no effect. I've

问题 I received this .h5 file from a friend and I need to use the data in it for some work. All the data is numerical. This the first time I work with these kind of files. I found many questions and answers here about reading these files but I couldn't find a way to get to lower level of the groups or folders the file contains. The file contains two main folders, i.e. X and Y X contains a folder named 0 which contains two folders named A and B. Y contains ten folders named 1-10. The data I want to

问题 I have a cable I am dropping from moving vehicle onto the ground. Using a camera system I estimate the location where the rope touches the ground in realtime. Movement of the vehicle and inaccuracy in the estimation of the location result in a point-cloud of touchdown locations. From this point cloud, I'd like to obain the most likely path of the cable lying on the ground. I'd like to achieve this in real-time, and I'd like the fit to be updated according to new data. The frequency of new

问题 I have a cable I am dropping from moving vehicle onto the ground. Using a camera system I estimate the location where the rope touches the ground in realtime. Movement of the vehicle and inaccuracy in the estimation of the location result in a point-cloud of touchdown locations. From this point cloud, I'd like to obain the most likely path of the cable lying on the ground. I'd like to achieve this in real-time, and I'd like the fit to be updated according to new data. The frequency of new

问题 I have a small, 3 layer, neural network with two input neurons, two hidden neurons and one output neuron. I am trying to stick to the below format of using only 2 hidden neurons. I am trying to show how this can be used to behave as the XOR logic gate, however with just two hidden neurons I get the following poor output after 1,000,000 iterations! Input: 0 0 Output: [0.01039096] Input: 1 0 Output: [0.93708829] Input: 0 1 Output: [0.93599738] Input: 1 1 Output: [0.51917667] If I use three

问题 I have a small, 3 layer, neural network with two input neurons, two hidden neurons and one output neuron. I am trying to stick to the below format of using only 2 hidden neurons. I am trying to show how this can be used to behave as the XOR logic gate, however with just two hidden neurons I get the following poor output after 1,000,000 iterations! Input: 0 0 Output: [0.01039096] Input: 1 0 Output: [0.93708829] Input: 0 1 Output: [0.93599738] Input: 1 1 Output: [0.51917667] If I use three

问题 I have a small, 3 layer, neural network with two input neurons, two hidden neurons and one output neuron. I am trying to stick to the below format of using only 2 hidden neurons. I am trying to show how this can be used to behave as the XOR logic gate, however with just two hidden neurons I get the following poor output after 1,000,000 iterations! Input: 0 0 Output: [0.01039096] Input: 1 0 Output: [0.93708829] Input: 0 1 Output: [0.93599738] Input: 1 1 Output: [0.51917667] If I use three

问题 I have a small, 3 layer, neural network with two input neurons, two hidden neurons and one output neuron. I am trying to stick to the below format of using only 2 hidden neurons. I am trying to show how this can be used to behave as the XOR logic gate, however with just two hidden neurons I get the following poor output after 1,000,000 iterations! Input: 0 0 Output: [0.01039096] Input: 1 0 Output: [0.93708829] Input: 0 1 Output: [0.93599738] Input: 1 1 Output: [0.51917667] If I use three