示例#1
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    def _compute(self, data_1, data_2):
        if self._sigma is None:
            # modification of libSVM heuristics
            self._sigma = float(data_1.shape[1])

        dists_sq = euclidean_dist_matrix(data_1, data_2)
        return np.exp(-np.sqrt(dists_sq) / self._sigma)
示例#2
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    def _compute(self, data_1, data_2):
        if self._sigma is None:
            # modification of libSVM heuristics
            self._sigma = float(data_1.shape[1])

        dists_sq = euclidean_dist_matrix(data_1, data_2)
        return np.exp(-np.sqrt(dists_sq) / self._sigma)
示例#3
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    def _compute(self, data_1, data_2):
        if self._gamma is None:
            # libSVM heuristics
            self._gamma = 1. / data_1.shape[1]

        dists_sq = euclidean_dist_matrix(data_1, data_2)
        return np.exp(-self._gamma * dists_sq)
示例#4
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    def _compute(self, data_1, data_2):
        if self._gamma is None:
            # libSVM heuristics
            self._gamma = 1./data_1.shape[1]

        dists_sq = euclidean_dist_matrix(data_1, data_2)
        return np.exp(-self._gamma * dists_sq)
示例#5
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    def _compute(self, data_1, data_2):

        dists_sq = euclidean_dist_matrix(data_1, data_2)
        return 1. - (dists_sq / (dists_sq + self._c))
示例#6
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 def _compute(self, data_1, data_2):
     return -euclidean_dist_matrix(data_1, data_2)**self._d / 2.
示例#7
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    def _compute(self, data_1, data_2):

        dists = np.sqrt(euclidean_dist_matrix(data_1, data_2))
        return 1 / (1 + dists**self._d)
示例#8
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 def _compute(self, data_1, data_2):
     
     dists_sq = euclidean_dist_matrix(data_1, data_2)
     return 1. - (dists_sq / (dists_sq + self._c))
示例#9
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 def _compute(self, data_1, data_2):
     return - euclidean_dist_matrix(data_1, data_2) ** self._d / 2.
示例#10
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    def _compute(self, data_1, data_2):

        dists = np.sqrt(euclidean_dist_matrix(data_1, data_2))
        return 1 / (1 + dists ** self._d)