torch.multiply: lambda input, other, out=None: -1, torch.multinomial: lambda input, num_samples, replacement=False, out=None: -1, torch.mv: lambda input, vec, out=None: -1, torch.mvlgamma: lambda input, p: -1, torch.narrow: lambda input, dim, start, length: -1, torch.nan_to...
dim(int) – the axis along which to index index(LongTensor) – the indices of elements to scatter, can be either empty or of the same dimensionality assrc. When empty, the operation returnsselfunchanged. src(Tensororfloat) – the source element(s) to scatter. 要填进去的元素 reduce(str,...
Multiply the audio by a random amplitude factor to reduce or increase the volume. This technique can help a model become somewhat invariant to the overall gain of the input audio. Warning: This transform can return samples outside the [-1, 1] range, which may lead to clipping or wrap dis...
You can also stack multiple "X"s on-top of each other, with corresponding multiplicity of loops, to multiply the thrust. This system is, in-fact, very flexible. The alpha particles, instead of being deflected, could be used to energize Hydrogen propellant, making a hybrid NTR. Or a ...
torch.bartlett_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor torch.blackman_window(window_length, periodic=True, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor ...
版权声明:本文为博主原创文章,遵循 CC 4.0 BY-SA 版权协议,转载请附上原文出处链接和本声明。 本文链接:https://blog.csdn.net/weixin_36670529/article/details/101205551 目录 Spectral Ops torch.fft(input, signal_ndim, normalized=False) → Tensor torch.ifft(input, signal_ndim, normalized=False) →...
Display the histogram of the samples, along with the probability density function: $ import matplotlib.pyplot as plt $ count, bins, ignored = plt.hist(s, 30, normed=True) $ x = np.arange(-8., 8., .01) $ pdf = np.exp(-abs(x-loc/scale))/(2.*scale) ...
(K, d)), axis=1) log_prob_data_given_components = -0.5 * ((d*np.log(2.0*math.pi) + log_det_Sigma).reshape(K, 1) + m_d) return PI.reshape(1, K) + log_prob_data_given_components.T def per_component_log_likelihood(self, x, sampled_features=None): """ Calculate per-...