def _predict_scores_fixed(self, X, **kwargs):
summary = dict(pm.summary(self.trace_)["mean"])
weights = np.zeros((self.n_object_features_fit_, self.n_mixtures))
for i, k in product(range(self.n_object_features_fit_), range(self.n_mixtures)):
weights[i][k] = summary["weights[{},{}]".format(i, k)]
utility = np.dot(X, weights)
p = np.mean(npu.softmax(utility, axis=1), axis=2)
return p
def _predict_scores_fixed(self, X, **kwargs):
mean_trace = dict(pm.summary(self.trace_)["mean"])
weights = np.array([
mean_trace["weights[{}]".format(i)]
for i in range(self.n_object_features_fit_)
])
lambda_k = np.array([
mean_trace["lambda_k[{}]".format(i)] for i in range(self.n_nests)
])
weights_ik = np.zeros((self.n_object_features_fit_, self.n_nests))
for i, k in product(range(self.n_object_features_fit_),
range(self.n_nests)):
weights_ik[i][k] = mean_trace["weights_ik[{},{}]".format(i, k)]
alpha_ik = np.dot(X, weights_ik)
alpha_ik = npu.softmax(alpha_ik, axis=2)
utility = np.dot(X, weights)
p = self._get_probabilities_np(utility, lambda_k, alpha_ik)
return p