from sklearn.manifold import TSNE
from sklearn.preprocessing import MinMaxScaler


def get_scaled_tsne_embeddings(features, perplexity, iteration):
    embedding = TSNE(n_components=2,
                     perplexity=perplexity,
                     n_iter=iteration).fit_transform(features)
    scaler = MinMaxScaler()
    scaler.fit(embedding)
    return scaler.transform(embedding)


tnse_embeddings_mfccs = []
tnse_embeddings_wavenet = []
perplexities = [2, 5, 30, 50, 100]
iterations = [200, 500, 1000, 2000, 5000]

for perplexity in perplexities:
    for iteration in iterations:
        tsne_mfccs = get_scaled_tsne_embeddings(mfcc_features,
                                                perplexity,
                                                iteration)
        tnse_wavenet = get_scaled_tsne_embeddings(wavenet_features,
                                                  perplexity,
                                                  iteration)