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| import numpy as np | |
| class Truth: | |
| def __init__(self, transformation, model): | |
| self.transformation = transformation | |
| self.weights = list(model.coef_) + [model.intercept_] | |
| def predict(self, X, y): | |
| transformed = self.transformation.transform(X) | |
| res = np.zeros(shape=y.shape) | |
| for w in range(len(self.weights)): | |
| if w < X.shape[1]: | |
| res = res + (X[:, w] * self.weights[w]) | |
| elif w == X.shape[1]: | |
| res = res + (y * self.weights[w]) | |
| else: | |
| assert w == X.shape[1] + 1 | |
| res = res + self.weights[w] | |
| return res | |
| def transform(self, X): | |
| return self.transformation.transform(X) | |
| def __str__(self): | |
| return f"Auxiliary Truth: {self.transformation} with linear coefficients for X, y, 1 {self.weights}" | |
| def __repr__(self): | |
| return str(self) | |
| def julia_string(self): | |
| """ | |
| Return an expression that sorta creates a julia instances of Truth with these parameters | |
| Specifically Truth(type, params, weights) | |
| Julia indexing starts at 1 not 0 so we need to add 1 to all parameter indices | |
| """ | |
| index = self.transformation.index | |
| params = self.transformation.get_params() | |
| return f"Truth({index}, {[param + 1 for param in params]}, {self.weights})" |