c06ed12c51cce046e7a6faa8876c0b26e3e02b11,pyglmnet.py,glm,fit,#glm#Any#Any#Any#Any#Any#,137

Before Change

        // Initialize parameters
        if self.distr=="multinomial":
            beta0_hat = np.random.normal(0.0,1.0, y.shape[1])
            beta_hat = np.random.normal(0.0, 1.0, [p, y.shape[1]])
        else:
            beta0_hat = np.random.normal(0.0,1.0,1)
            beta_hat = np.random.normal(0.0,1.0,[p,1])
        fit = []

After Change

            // convert to 1-hot encoding
            y_bk = y
            y = np.zeros([x.shape[0], y.max()+1])
            for i in range(x.shape[0]):
                y[i, y_bk[i]] = 1.

        // number of predictions
        if self.distr=="multinomial":
            k = y.shape[1]
        else:
            k = 1

Instances