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def update_user_to_product(self, label: ProductLabel, value: float) -> None:
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raise ValueError("Hyper parameter epsilon must be in (0, 0.5):", epsilon)
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def psi(u_label: UserLabel, p_label: ProductLabel, r_label: ReviewLabel, epsilon: float) -> float:
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# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
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This helper function computes a logarithm of the product of messages such as
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sum_reviewer = self.retrieve_review(reviewer, product).user_to_product(p_label)
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"""Compute an updated message from a product to a user with a user label.
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where :math:`\\cal{P}` is a set of produce nodes, :math:`\\cal{L}_{\\cal{P}}`
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man_pages = [(master_doc, "reviewgraphminingfraudeagle", "Review Graph Mining Fraud Eagle Documentation", [author], 1)]
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To implement those message functions, this review class maintain four values
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"""Message function from the user to the product associated with this review.
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message_to_product[p_label] = self._update_user_to_product(reviewer, product, p_label)
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def _update_product_to_user(self, reviewer: Reviewer, product: Product, u_label: UserLabel) -> float:
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.. _ICWSM 2013: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/viewFile/5981/6338
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b[u_label] = phi_u(u_label) + self.graph.prod_message_from_products(self, None, u_label)
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return cast(float, np.exp(b[UserLabel.FRAUD] - _logaddexp(*b.values())))
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+ "\n".join(" {0}-{1}: {2}".format(edges[i], edges[i + 1], v) for i, v in enumerate(histo))
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return cast(float, np.sum([self.retrieve_review(reviewer, p).product_to_user(u_label) for p in products]))
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def prod_message_from_products(self, reviewer: Reviewer, product: Optional[Product], u_label: UserLabel) -> float:
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