Python scipy.sort() Examples

The following are 2 code examples of scipy.sort(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module scipy , or try the search function .
Example #1
Source File: process_data.py    From GPPVAE with Apache License 2.0 5 votes vote down vote up 
def import_data(size=128):

    files = []
    orients = ["00F", "30L", "30R", "45L", "45R", "60L", "60R", "90L", "90R"]
    for orient in orients:
        _files = glob.glob(os.path.join(data_dir, "*/*_%s.jpg" % orient))
        files = files + _files
    files = sp.sort(files)

    D1id = []
    D2id = []
    Did = []
    Rid = []
    Y = sp.zeros([len(files), size, size, 3], dtype=sp.uint8)
    for _i, _file in enumerate(files):
        y = imread(_file)
        y = imresize(y, size=[size, size], interp="bilinear")
        Y[_i] = y
        fn = _file.split(".jpg")[0]
        fn = fn.split("/")[-1]
        did1, did2, rid = fn.split("_")
        Did.append(did1 + "_" + did2)
        Rid.append(rid)
    Did = sp.array(Did, dtype="|S100")
    Rid = sp.array(Rid, dtype="|S100")

    RV = {"Y": Y, "Did": Did, "Rid": Rid}
    return RV
Example #2
Source File: distanceratioexperiment.py    From aurum-datadiscovery with MIT License 4 votes vote down vote up 
def __init__(self, N, vectors, coverage_ratio=0.2):
        """
        Performs exact nearest neighbour search on the data set.

        vectors can either be a numpy matrix with all the vectors
        as columns OR a python array containing the individual
        numpy vectors.
        """
        # We need a dict from vector string representation to index
        self.vector_dict = {}
        self.N = N
        self.coverage_ratio = coverage_ratio

        # Get numpy array representation of input
        self.vectors = numpy_array_from_list_or_numpy_array(vectors)

        # Build map from vector string representation to vector
        for index in range(self.vectors.shape[1]):
            self.vector_dict[self.__vector_to_string(
                self.vectors[:, index])] = index

        # Get transposed version of vector matrix, so that the rows
        # are the vectors (needed by cdist)
        vectors_t = numpy.transpose(self.vectors)

        # Determine the indices of query vectors used for comparance
        # with approximated search.
        query_count = numpy.floor(self.coverage_ratio *
                                  self.vectors.shape[1])
        self.query_indices = []
        for k in range(int(query_count)):
            index = numpy.floor(k * (self.vectors.shape[1] / query_count))
            index = min(index, self.vectors.shape[1] - 1)
            self.query_indices.append(int(index))

        print('\nStarting exact search (query set size=%d)...\n' % query_count)

        # For each query vector get radius of closest N neighbours
        self.nearest_radius = {}
        self.exact_search_time_per_vector = 0.0

        for index in self.query_indices:

            v = vectors_t[index, :].reshape(1, self.vectors.shape[0])
            exact_search_start_time = time.time()
            D = cdist(v, vectors_t, 'euclidean')

            # Get radius of closest N neighbours
            self.nearest_radius[index] = scipy.sort(D)[0, N]

            # Save time needed for exact search
            exact_search_time = time.time() - exact_search_start_time
            self.exact_search_time_per_vector += exact_search_time

        print('\Done with exact search...\n')

        # Normalize search time
        self.exact_search_time_per_vector /= float(len(self.query_indices))