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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _initial_estimate(self, y, modelmat):
 """ Makes an inital estimate for the model coefficients. For a LinearGAM we simply initialize to small coefficients. For other GAMs we transform the problem to the linear space and solve an unpenalized version. Parameters --------- y : array-like of shape (n,) containing target data modelmat : sparse matrix of shape (n, m) containing model matrix of the spline basis Returns ------- coef : array of shape (m,) containing the initial estimate for the model coefficients Notes ----- This method implements the suggestions in Wood, section 2.2.2 Geometry and IRLS convergence, pg 80 """


        # do a simple initialization for LinearGAMs
        if isinstance(self, LinearGAM):
            n, m = modelmat.shape
            return np.ones(m) * np.sqrt(EPS)

        # transform the problem to the linear scale
        y = deepcopy(y).astype('float64')
        y[y == 0] += .01 # edge case for log link, inverse link, and logit link
        y[y == 1] -= .01 # edge case for logit link

        y_ = self.link.link(y, self.distribution)
        y_ = make_2d(y_, verbose=False)
        assert np.isfinite(y_).all(), "transformed response values should be well-behaved."

        # solve the linear problem
        return np.linalg.solve(load_diagonal(modelmat.T.dot(modelmat).A),
                               modelmat.T.dot(y_))