migrate to ghactions for ci/cd
sauln
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"""
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Implementation of the "multiscale heat kernel" (CVPR 2015),
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Author: Chris Tralie
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"""
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import numpy as np |
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__all__ = ["heat"] |
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def evalHeatKernel(dgm1, dgm2, sigma): |
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"""
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Evaluate the continuous heat-based kernel between dgm1 and dgm2 (more correct than L2 on the discretized version above but may be slower because can't exploit fast matrix multiplication when evaluating many, many kernels)
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"""
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kSigma = 0 |
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I1 = np.array(dgm1) |
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I2 = np.array(dgm2) |
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for i in range(I1.shape[0]): |
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p = I1[i, 0:2] |
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for j in range(I2.shape[0]): |
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q = I2[j, 0:2] |
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qc = I2[j, 1::-1] |
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kSigma += np.exp(-(np.sum((p - q) ** 2)) / (8 * sigma)) - np.exp( |
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-(np.sum((p - qc) ** 2)) / (8 * sigma) |
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)
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return kSigma / (8 * np.pi * sigma) |
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def heat(dgm1, dgm2, sigma=0.4): |
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"""
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Return the pseudo-metric between two diagrams based on the continuous
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heat kernel as described in "A Stable Multi-Scale Kernel for Topological Machine Learning" by Jan Reininghaus, Stefan Huber, Ulrich Bauer, and Roland Kwitt (CVPR 2015)
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Parameters
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-----------
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dgm1: np.array (m,2)
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A persistence diagram
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dgm2: np.array (n,2)
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A persistence diagram
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sigma: float
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Heat diffusion parameter (larger sigma makes blurrier)
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Returns
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--------
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dist: float
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heat kernel distance between dgm1 and dgm2
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"""
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return np.sqrt( |
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evalHeatKernel(dgm1, dgm1, sigma) |
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+ evalHeatKernel(dgm2, dgm2, sigma) |
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- 2 * evalHeatKernel(dgm1, dgm2, sigma) |
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)
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Read our documentation on viewing source code .