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 ...
for somem \times msum-of-squares (SOS) polynomial matricesS_0,\,\ldots ,\,S_q. A polynomial matrixS(x) is SOS ifS(x)=H(x)^{{\mathsf T}}H(x)for some polynomial matrixH(x), and it is well known [12,18,34,42] that linear optimization problems with SOS matrix variables can ...
Given the definition of a 2D array such as the following String[][] data={ {"A","B"}, {"1","2"}, {"XX","YY", "ZZ"} }; write a recursive program that outputs all combinations of each subarray in orde 1. To learn how nested for loop...
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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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 ...