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Compute least-square, multivariate linear regression on the input data, i.e., learn coefficients b_j so that y_i = b_0 + b_1 x_1 + ... b_N x_N , for i = 1 ... M, minimizes the square error given the training x's and y's. This is a supervised learning node, and requires input data x and target data y to be supplied during training (see 'train' docstring). Internal variables of interest: self.beta -- the coefficients of the linear regression
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__metaclass__ This Metaclass is meant to overwrite doc strings of methods like execute, stop_training, inverse with the ones defined in the corresponding private methods _execute, _stop_training, _inverse, etc... |
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_train_seq List of tuples: [(training-phase1, stop-training-phase1), (training-phase2, stop_training-phase2), ... |
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dtype dtype |
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input_dim Input dimensions |
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output_dim Output dimensions |
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supported_dtypes Supported dtypes |
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Input arguments: with_bias -- If True, the linear model includes a constant term True: y_i = b_0 + b_1 x_1 + ... b_N x_N False: y_i = b_1 x_1 + ... b_N x_N If present, the constant term is stored in the first column of self.beta use_pinv -- If true, uses the pseudo-inverse function to compute the linear regression coefficients, which is more robust in some cases
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Add a constant term to the vector 'x'. x -> [1 x] |
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Return the list of dtypes supported by this node. The types can be specified in any format allowed by numpy.dtype.
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Additional input arguments: y -- array of size (x.shape[0], output_dim) that contains the observed output to the input x's.
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Return True if the node can be inverted, False otherwise.
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Additional input arguments: y -- array of size (x.shape[0], output_dim) that contains the observed output to the input x's.
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