def run(self, test):
p = self.get_distance_matrix(test)
p *= -1.0 # no argmin supported yet
idx = cp.dev_tensor_uint(test.shape[0])
cp.reduce_to_row(idx, p, cp.reduce_functor.ARGMAX)
hidx = idx.np.reshape(idx.shape[0])
return self.data_l.reshape(self.data.shape[0])[hidx]
def run(self,test):
p = self.get_distance_matrix(test)
p *= -1. # no argmin supported yet
idx = cp.dev_tensor_uint(test.shape[0])
cp.reduce_to_row(idx, p, cp.reduce_functor.ARGMAX)
hidx = idx.np.reshape(idx.shape[0])
return self.data_l.reshape(self.data.shape[0])[hidx]
def kmeans(dataset, num_clusters, iters):
# initialize clusters randomly
rand_indices = np.random.randint(0, dataset.shape[0], num_clusters)
clusters = dataset[rand_indices,:]
# push initial clusters and dataset to device
dataset_dev = cp.dev_tensor_float(dataset)
clusters_dev = cp.dev_tensor_float(clusters)
# allocate matrices for calculations (so we don't need to allocate in loop)
dists = cp.dev_tensor_float([dataset_dev.shape[0], num_clusters])
nearest = cp.dev_tensor_uint(dataset_dev.shape[0])
# main loop
for i in xrange(iters):
# compute pairwise distances
cp.pdist2(dists, dataset_dev, clusters_dev)
# find closest cluster
cp.reduce_to_col(nearest, dists, cp.reduce_functor.ARGMIN)
# update cluster centers
# (this is a special purpose function for kmeans)
cp.compute_clusters(clusters_dev, dataset_dev, nearest)
return [clusters_dev.np, nearest.np]
