type, X = dataset_class(**DATASETS.get(dataset, {}))
for algo_name in algorithms:
assert(algo_name in ALGORITHMS, "Unknown algorithm: %s" % algo_name)
title = algo_name.title() + "-" + dataset.title()
algo_class = getattr(algos, algo_name)
algo = algo_class(n_components=2, **ALGORITHMS.get(algo_name, {}))
try:
logging.info("Running " + " ".join(title.split("-")))
if type == ARTIFICIAL:
X_data = X[0]
X_data.dtype='float64'
X_reduced = algo.run(X_data)
plot_artificial_dataset(X_data, X_reduced, color=X[1], title=title, save=args.save, saveDir=output_dir)
else:
X_data = X["data"]
X_reduced = algo.run(X_data)
# For 2 dimensional
plot_embedding(X_reduced, X["target"], X["images"], X['target'], title, save=args.save, saveDir=output_dir)
if args.threeD:
# For 3 dimensional
algo = algo_class(n_components=3, n_neighbors=30)
X_reduced = algo.run(X_data)
title = title + "-" + "3D"
plot_embedding_3D(X_reduced, X["target"], title, save=args.save, saveDir=output_dir)
except Exception, e:
for i in range(numNodes):
for j in range(numNodes):
if k_neighbors_array[j, i] <= k_neighbors_array[i, j]:
k_neighbors_array[i, j] = k_neighbors_array[j, i]
else:
k_neighbors_array[j, i] = k_neighbors_array[i, j]
# Compute the all pair shortest path distance.
dist_matrix = floyd_warshall(k_neighbors_array, directed=False)
dist_matrix[np.isinf(dist_matrix)] = 0
# Do MDS or learn embedding
# MDS can also be seen as a case of Kernel PCA
# using data dependent kernel
# So using K = 1/2 D^2,
# we generate projections along principal components
kernel = dist_matrix ** 2
kernel *= -0.5
kernelPCA = KernelPCA(n_components=self.n_components, kernel='precomputed')
return kernelPCA.fit_transform(kernel)
if __name__ == "__main__":
isomap = Isomap(10, 3)
X, color = datasets.make_swiss_roll(n_samples=3000)
X_r = isomap.run(X)
plot_artificial_dataset(X, X_r, color, "Swiss Roll")
