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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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_train_seq List of tuples:: |
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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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: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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**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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Process the data contained in `x`. If the object is still in the training phase, the function `stop_training` will be called. `x` is a matrix having different variables on different columns and observations on the rows. By default, subclasses should overwrite `_execute` to implement their execution phase. The docstring of the `_execute` method overwrites this docstring.
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Return True if the node can be inverted, False otherwise.
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Stop the training phase. By default, subclasses should overwrite `_stop_training` to implement this functionality. The docstring of the `_stop_training` method overwrites this docstring.
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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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