Xnew is a data matrix, point at which we want to predict This method computes p(F* | (F=LV) ) where F* are points on the GP at Xnew, F=LV are points on the GP at X.
(
self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False
)
| 107 | |
| 108 | @inherit_check_shapes |
| 109 | def predict_f( |
| 110 | self, Xnew: InputData, full_cov: bool = False, full_output_cov: bool = False |
| 111 | ) -> MeanAndVariance: |
| 112 | """ |
| 113 | Xnew is a data matrix, point at which we want to predict |
| 114 | |
| 115 | This method computes |
| 116 | |
| 117 | p(F* | (F=LV) ) |
| 118 | |
| 119 | where F* are points on the GP at Xnew, F=LV are points on the GP at X. |
| 120 | |
| 121 | """ |
| 122 | assert_params_false(self.predict_f, full_output_cov=full_output_cov) |
| 123 | |
| 124 | X_data, _Y_data = self.data |
| 125 | mu, var = conditional( |
| 126 | Xnew, X_data, self.kernel, self.V, full_cov=full_cov, q_sqrt=None, white=True |
| 127 | ) |
| 128 | return mu + self.mean_function(Xnew), var |
nothing calls this directly
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