Merge remote-tracking branch 'origin/master'
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commit
7fefb6a8d3
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@ -318,7 +318,11 @@ def kernel_point_optimization_debug(radius, num_points, num_kernels=1, dimension
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saved_gradient_norms = np.zeros((10000, num_kernels))
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old_gradient_norms = np.zeros((num_kernels, num_points))
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for iter in range(10000):
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step = -1
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while step < 10000:
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# Increment
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step += 1
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# Compute gradients
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# *****************
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@ -344,7 +348,7 @@ def kernel_point_optimization_debug(radius, num_points, num_kernels=1, dimension
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# Compute norm of gradients
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gradients_norms = np.sqrt(np.sum(np.power(gradients, 2), axis=-1))
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saved_gradient_norms[iter, :] = np.max(gradients_norms, axis=1)
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saved_gradient_norms[step, :] = np.max(gradients_norms, axis=1)
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# Stop if all moving points are gradients fixed (low gradients diff)
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@ -372,7 +376,7 @@ def kernel_point_optimization_debug(radius, num_points, num_kernels=1, dimension
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kernel_points -= np.expand_dims(moving_dists, -1) * gradients / np.expand_dims(gradients_norms + 1e-6, -1)
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if verbose:
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print('iter {:5d} / max grad = {:f}'.format(iter, np.max(gradients_norms[:, 3:])))
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print('step {:5d} / max grad = {:f}'.format(step, np.max(gradients_norms[:, 3:])))
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if verbose > 1:
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plt.clf()
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plt.plot(kernel_points[0, :, 0], kernel_points[0, :, 1], '.')
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@ -389,6 +393,10 @@ def kernel_point_optimization_debug(radius, num_points, num_kernels=1, dimension
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# moving factor decay
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moving_factor *= continuous_moving_decay
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# Remove unused lines in the saved gradients
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if step < 10000:
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saved_gradient_norms = saved_gradient_norms[:step+1, :]
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# Rescale radius to fit the wanted ratio of radius
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r = np.sqrt(np.sum(np.power(kernel_points, 2), axis=-1))
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kernel_points *= ratio / np.mean(r[:, 1:])
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