561ec05d900a6cf6092c2b15aae069d81717e99a,fastTSNE/tsne.py,TSNE,generate_initial_coordinates,#TSNE#Any#Any#,363

Before Change

        // number of components
        elif initialization == "pca":
            pca = PCA(n_components=self.n_components)
            return pca.fit_transform(X)

        // Random initialization with isotropic normal distribution
        elif initialization == "random":
            return np.random.normal(0, 1e-2, (X.shape[0], self.n_components))

After Change

        // number of components
        elif initialization == "pca":
            pca = PCA(n_components=self.n_components)
            embedding = pca.fit_transform(X)
            // The PCA embedding may have high variance, which leads to poor convergence
            normalization = np.std(embedding, axis=0) * 100
            embedding /= normalization

Instances