1. Declare four 2D array of some fixed capacity. 2 for storing data, one for storing sum of the elements of first two matrices, one for storing difference of first two matrices. 2. Take the number of rows and columns as input from users. Accordingly define the elements of first two ar...
Example #2Source File: 5_nueral_network.py From deep-learning-note with MIT License 6 votes def cost(params, input_size, hidden_size, num_labels, X, y, learning_rate): m = X.shape[0] X = np.matrix(X) y = np.matrix(y) # reshape the parameter array into parameter matrices ...
Because for every row returns a number, the result is a vector of values that represent the magnitude of the reduce add operation for every row. Template params: T: type of the operation; LEN: number of elements to be processed in the kernel per iteration; INCREMENT: parameter that ...
Actually a sum over a 3D array along the 3rd dimension replies a [M x N x 1] array, as summing over the 2nd dimension creates a [M x 1 X P] array. But Matlab ignores trailing singelton dimensions, except it is the 2nd dimension of a matrix (known as "column vector").
示例2: _lmvnpdffull ▲点赞 6▼ def_lmvnpdffull(obs, means, covars):""" Log probability for full covariance matrices. WARNING: In certain cases, this function will modify in-place some of the covariance matrices """fromscipyimportlinalgimportitertoolsifhasattr(linalg,'solve_triangular'):# onl...
In addition, the Gaussian-type assumptions made when estimating the confidence intervals from the sample covariance matrices might be violated in practice. Monte-Carlo analysis provides much more flexibility in terms of designing the experiments since the data reduction equation is already implemented for...
In Section 2, the 2D data model and the concept of difference coarray are overviewed. Then, the structure of prototype planar coprime array is introduced. In Section 3, the 2D VCAM algorithm is proposed to generate the diff-sum coarray. Two 2D coprime configurations, i.e., CAACS and ...
This technique, complemented by a unique T-shaped circular distributed microphone array, aims to enhance 3D multiple simultaneous SSL. The 3D location of each acoustic source is estimated by finding the closest point to all estimated DOAs. The work outlined in [17] addresses the challenge of ...
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"" # note : we sum over the size of a datapoint; if we are using # minibatches, L will be a vector, withd one entry per # example in minibatch network_output = self.get_output() temp = T.pow(center - network_output, 2) L = T.sum(temp, axis=1) # Add the network ...