Example #1
0
def test():
    ###########################################################################
    ############################# Artificial data #############################
    ###########################################################################
    ## Random relations
    n, density, shape = 100, 0.1, (10, 10)
    randint_sparse_matrix(density, shape, maxvalue=10)
    generate_randint_relations(density, shape, p0=0., maxvalue=1)
    generate_random_relations_cutoffs(n, 0.5, 0.9, True, 'network')
    generate_random_relations_cutoffs(n, 0.5, 0.9, False, 'network')
    generate_random_relations_cutoffs(n, 0.5, 0.9, True, 'sparse')

    n_elements, n_collections = 100, 10
    random_membership(n_elements, n_collections, multiple=True)
    random_membership(n_elements, n_collections, multiple=False)

    ## Random points
    n_points, n_dim, funct = 100, 2, np.cos
    random_transformed_space_points(n_points, n_dim, funct)
    random_transformed_space_points(n_points, n_dim, None)
    random_space_points(n_points, n_dim)

    ## Artificial grid data
    create_random_image(shape, n_modes=1)
    create_random_image(shape, n_modes=3)

    ## Artificial regions
    n_poly = 10
    random_shapely_polygon(bounding=(None, None), n_edges=0)
    random_shapely_polygon(bounding=((0., 1.), None), n_edges=0)
    random_shapely_polygon(bounding=(None, None), n_edges=4)
    random_shapely_polygons(n_poly, bounding=(None, None), n_edges=0)

    ## Artificial random features
    n, n_feats = np.random.randint(10, 1000), np.random.randint(2, 20)
    n_feats2 = [np.random.randint(2, 20) for i in range(n_feats)]
    ks = np.random.randint(1, 20)

    feats = continuous_array_features(n, n_feats)
    assert(len(feats.shape) == 2)
    feats = categorical_array_features(n, n_feats)
    assert(len(feats.shape) == 2)
    feats = categorical_array_features(n, n_feats2)
    assert(len(feats.shape) == 2)
    feats = continuous_dict_features(n, n_feats)
    assert(type(feats[0]) == dict)
    feats = categorical_dict_features(n, n_feats)
    assert(type(feats[0]) == dict)

    feats = continuous_agg_array_features(n, n_feats, ks)
    assert(len(feats.shape) == 3)
    feats = categorical_agg_array_features(n, n_feats, ks)
    assert(len(feats.shape) == 3)
    feats = categorical_agg_array_features(n, n_feats2, ks)
    assert(len(feats.shape) == 3)
    feats = continuous_agg_dict_features(n, n_feats, ks)
    assert(type(feats[0][0]) == dict)
    feats = categorical_agg_dict_features(n, n_feats, ks)
    assert(type(feats[0][0]) == dict)

    ## Artificial measures
    n_vals_i, n_iss = np.random.randint(2, 30), np.random.randint(1, 30)

    create_empty_features_array(n_feats, n_iss, ks)
    create_empty_features_dict(n_feats, n_iss, ks)
    create_features_i_array(n_feats, n_iss, ks)
    create_features_i_dict(n_feats, n_iss, ks)

    create_vals_i(n_iss, n_vals_i, ks)

    create_empty_array(ks, n_vals_i, n_feats)
    create_empty_append(ks, n_iss, n_feats)
    create_empty_replacelist(ks, n_iss, n_feats)

    create_artificial_measure_array(ks, n_vals_i, n_feats)
    create_artificial_measure_append(ks, n_vals_i, n_feats)
    create_artificial_measure_replacelist(ks, n_vals_i, n_feats)
    create_artificial_measure_replacelist(ks, n_vals_i, n_feats, True)

    ###########################################################################
    ############################ Spatial Elements #############################
    ###########################################################################
    ## Parameters
    words = m.replace('\n', ' ').replace('.', ' ').strip().split(" ")
    ids = [hash(e) for e in words]
    functs = [lambda x: str(x)+words[i] for i in range(len(words))]
    regs = random_shapely_polygons(10, bounding=(None, None), n_edges=0)

    ## Testing Elemets
    words_id = np.arange(len(words))
    words_elements = SpatialElementsCollection(words, words_id)
    words_elements2 = SpatialElementsCollection(words, list(words_id))
    words_elements = SpatialElementsCollection(words)
    ids_elements = SpatialElementsCollection(ids)
    functs_elements = SpatialElementsCollection(functs)
    polys_elements = SpatialElementsCollection(regs, np.arange(len(regs)))

    # Testing error instantiation
    try:
        flag_error = False
        SpatialElementsCollection(0)
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")
    try:
        flag_error = False
        SpatialElementsCollection(words, np.arange(len(words)+1))
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")
    try:
        flag_error = False
        tags = range(len(words)) + [len(words)-1]
        SpatialElementsCollection(words, tags)
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")
    try:
        flag_error = False
        SpatialElementsCollection(words, 5)
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")

    # Class functions
    words_elements[0]
    try:
        flag_error = False
        words_elements[len(words_elements)]
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")
    try:
        flag_error = False
        words_elements2[words[0]]
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")

    words_elements.elements_id = None
    try:
        flag_error = False
        words_elements[words[0]]
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")

    words_elements[0]

    for e in words_elements:
        pass

    for e in words_elements2:
        pass

    words_elements == words[0]
    relabel_map = np.arange(len(words))
    try:
        flag_error = False
        words_elements.relabel_elements(range(len(words)))
        flag_error = True
    except:
        if flag_error:
            raise Exception("It has to halt here.")

    words_elements.relabel_elements(relabel_map)
    relabel_map = dict(zip(relabel_map, relabel_map))
    words_elements.relabel_elements(relabel_map)

    ids_elements[0]
    for e in ids_elements:
        pass
    ids_elements == words[0]

    functs_elements[0]
    for e in functs_elements:
        pass
    functs_elements == words[0]

    # Polygon collections
    polys_elements == polys_elements[0]

    ############################ Locations Object #############################
    ###########################################################################
    ## Locations
    locs1 = np.random.random((100, 5))
    locs2 = np.random.random((100, 1))
    locs3 = np.random.random(100)
    locs4 = np.random.random((100, 2))
    sptrans = lambda x, p: np.sin(x)

    class Translocs:
        def __init__(self):
            pass

        def apply_transformation(self, x, p={}):
            return sptrans(x, p)
    sptrans2 = Translocs()

    lspcol = SpatialElementsCollection(locs1, np.arange(len(locs1)))
    lspcol == lspcol[0]

    try:
        flag_error = False
        locs = Locations(locs1, 5)
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")
    try:
        flag_error = False
        locs = Locations(locs1, list(range(len(locs1)+1)))
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")
    try:
        flag_error = False
        tags = list(range(len(locs1)))
        tags[0] = 1
        locs = Locations(locs1, tags)
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")

    locs = Locations(locs1)
    locsbis = Locations(locs1, list(range(len(locs1))))
    for l in locs:
        pass
    try:
        flag_error = False
        locs[-1]
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")
    try:
        flag_error = False
        locsbis[slice(0, 9)]
        flag_error = True
        raise Exception("It has to halt here.")
    except:
        if flag_error:
            raise Exception("It has to halt here.")

    locsbis[0]
    locs[0]
    assert((locs == locs1[0])[0])
    locs.compute_distance(locs[1])
    locs.space_transformation(sptrans, {})
    locs.space_transformation(sptrans2, {})
    locs._check_coord(0)
    locs._check_coord(locs[0])
    locs._check_coord([0, 3])
    locs._check_coord(np.random.random(locs.locations.shape[1]))
    locs._check_coord([locs1[0], locs1[3]])
    locs._check_coord(None)
    locs.in_radio(locs[0], 0.2)
    locs.data

    locs = Locations(locs2)
    assert((locs == locs2[0])[0])
    locs.compute_distance(locs[1])
    locs.space_transformation(sptrans, {})
    locs.space_transformation(sptrans2, {})
    locs.in_manhattan_d(locs[0], 0.2)

    locs = Locations(locs3)
    assert((locs == locs3[0])[0])
    locs.compute_distance(locs[1])
    locs.space_transformation(sptrans, {})
    locs.space_transformation(sptrans2, {})

    locs = Locations(locs4)
    locs.in_block_distance_d(np.random.random((1, 2)), 0.2)

    ###########################################################################
    ############################### Membership ################################
    ###########################################################################
    # artificial data
    random_membership(10, 20, True)
    random_membership(10, 20, False)

    n_in, n_out = 100, 20
    relations = [np.unique(np.random.randint(0, n_out,
                                             np.random.randint(n_out)))
                 for i in range(n_in)]
    relations = [list(e) for e in relations]
    memb1 = Membership(relations)

    memb1.to_network()
    memb1.to_dict()
    memb1.to_sparse()
    memb1.reverse_mapping()
    memb1.getcollection(0)
    memb1.getcollection(memb1.max_collection_id-1)
    memb1.collections_id
    memb1.n_collections
    memb1.n_elements
    memb1.membership
    str(memb1)
    memb1[0]
    memb1 == 0
    for e in memb1:
        pass

#    op2 = np.all([t == dict for t in types])
    relations = [dict(zip(e, len(e)*[{'membership': 1}])) for e in relations]
    memb1_dict = Membership(relations)
    memb1_dict.to_network()
    memb1_dict.to_dict()
    memb1_dict.to_sparse()
    memb1_dict.reverse_mapping()
    memb1_dict.getcollection(0)
    memb1.getcollection(memb1.max_collection_id-1)
    memb1_dict.collections_id
    memb1_dict.n_collections
    memb1_dict.n_elements
    memb1_dict.membership
    memb1.shape
    memb1.max_collection_id

    memb2 = Membership(np.random.randint(0, 20, 100))
    memb2.to_network()
    memb2.to_dict()
    memb2.to_sparse()
    memb2.reverse_mapping()
    memb2.getcollection(0)
    memb2.getcollection(memb2.max_collection_id-1)
    memb2.collections_id
    memb2.n_collections
    memb2.n_elements
    memb2.membership
    str(memb2)
    memb2[0]
    memb2 == 0
    for e in memb2:
        pass
    memb2.shape
    memb2.max_collection_id

    sparse = randint_sparse_matrix(0.2, (200, 100), 1)
    memb3 = Membership(sparse)
    memb3.to_dict()
    memb3.to_network()
    memb3.to_sparse()
    memb3.reverse_mapping()
    memb3.getcollection(0)
    memb3.getcollection(memb3.max_collection_id-1)
    memb3.collections_id
    memb3.n_collections
    memb3.n_elements
    memb3.membership
    str(memb3)
    memb3[0]
    memb3 == 0
    for e in memb3:
        pass
    memb3.shape
    memb3.max_collection_id

    relations = [[np.random.randint(10)] for i in range(50)]
    memb4 = Membership(relations)
    memb4.to_network()
    memb4.to_dict()
    memb4.to_sparse()
    memb4.reverse_mapping()
    memb4.getcollection(0)
    memb4.getcollection(memb4.max_collection_id-1)
    memb4.collections_id
    memb4.n_collections
    memb4.n_elements
    memb4.membership
    str(memb4)
    memb4[0]
    memb4 == 0
    for e in memb4:
        pass
    memb4.shape
    memb4.max_collection_id

    relations[0].append(0)
    memb5 = Membership(relations)
    memb5.to_network()
    memb5.to_dict()
    memb5.to_sparse()
    memb5.reverse_mapping()
    memb5.getcollection(0)
    memb5.getcollection(memb5.max_collection_id-1)
    memb5.collections_id
    memb5.n_collections
    memb5.n_elements
    memb5.membership
    str(memb5)
    memb5[0]
    memb5 == 0
    for e in memb5:
        pass
    memb5.shape
    memb5.max_collection_id

    relations[0].append(0)
    memb6 = Membership((sparse, np.arange(100)))
    memb6.to_network()
    memb6.to_dict()
    memb6.to_sparse()
    memb6.reverse_mapping()
    memb6.getcollection(0)
    memb6.getcollection(memb6.max_collection_id-1)
    memb6.collections_id
    memb6.n_collections
    memb6.n_elements
    memb6.membership
    str(memb6)
    memb6[0]
    memb6 == 0
    for e in memb6:
        pass
    memb6.shape
    memb6.max_collection_id

    ###########################################################################
    ############################### Mapper vals ###############################
    ###########################################################################
    feat_arr0 = np.random.randint(0, 20, 100)

    def map_vals_i_t(s, i, k):
        k_p, k_i = s.features[0]._map_perturb(k)
        i_n = s.features[0]._perturbators[k_p].apply2indice(i, k_i)
        return feat_arr0[i_n].ravel()[0]
    map_vals_i = create_mapper_vals_i(map_vals_i_t, feat_arr0)

    # correlation
    map_vals_i = create_mapper_vals_i('correlation', feat_arr0)
    map_vals_i = create_mapper_vals_i(('correlation', 100, 20), feat_arr0)
    map_vals_i = create_mapper_vals_i('matrix')
    map_vals_i = create_mapper_vals_i('matrix', feat_arr0)
    map_vals_i = create_mapper_vals_i('matrix', feat_arr0.reshape((100, 1)))
    map_vals_i = create_mapper_vals_i(('matrix', 20), list(feat_arr0))
    map_vals_i = create_mapper_vals_i(('matrix', 100, 20), len(feat_arr0))
    map_vals_i = create_mapper_vals_i('matrix', slice(0, 100, 1))
    map_vals_i.set_prefilter(slice(0, 100, 1))
    map_vals_i.set_prefilter(10)
    map_vals_i.set_prefilter([0, 2])
    map_vals_i.set_sptype('correlation')
    map_vals_i[(None, [0], 0)]
    map_vals_i.apply(None, [0], 0)

    map_vals_i = create_mapper_vals_i(map_vals_i)
    map_vals_i = create_mapper_vals_i(feat_arr0.reshape(100, 1))
    map_vals_i = create_mapper_vals_i(None)

    map_vals_i = Map_Vals_i(100)
    map_vals_i = Map_Vals_i((1000, 20))
    map_vals_i = Map_Vals_i(map_vals_i)
    map_vals_i = Map_Vals_i(memb1)

    ## Stress testing
    try:
        boolean = False
        map_vals_i = create_mapper_vals_i('correlation')
        boolean = True
        raise Exception("It has to halt here.")
    except:
        if boolean:
            raise Exception("The test has to halt here.")

    ###########################################################################
    ############################## Spdesc_mapper ##############################
    ###########################################################################
    #selector1 = Sp_DescriptorMapper()
    mapper_array = np.random.randint(0, 5, 100)
    mapper_function = lambda idx: mapper_array[idx]
    mapper_function1 = lambda idx: tuple([mapper_array[idx]]*2)

    pos_mappers = [{'mapper': mapper_array}, {'mapper': mapper_function},
                   {'mapper': mapper_function, 'compute': True},
                   {'mapper': mapper_function, 'n_in': 5, 'n_out': 6},
                   {'mapper': mapper_function1, 'n_in': 5, 'n_out': [3, 4]},
                   {'mapper': mapper_function1, 'n_in': 5, 'compute': True}]

    for p in pos_mappers:
        comb_selector = DummySelector(**p)
#        comb_selector = GeneralSelector(**p)
        comb_selector[0]

        # Impossible cases
        try:
            ## Non-integer key getitem
            boolean = False
            map_vals_i = comb_selector[.2]
            boolean = True
            raise Exception("It has to halt here.")
        except:
            if boolean:
                raise Exception("The test has to halt here.")

        ## Functions
        DummySelector(comb_selector)
        comb_selector[0]
        comb_selector.set_pars(2, lambda x: (0, 0), n_out=[1, 1])
        comb_selector[0]

    selector1 = DummySelector(mapper_array)
    selector2 = DummySelector(lambda idx: mapper_array[idx], n_in=100, n_out=3)
    selector3 = DummySelector(lambda idx: [mapper_array[idx]]*3, n_in=100)
    sl = BaseCollectionSelectors([selector1, selector2, selector3])

    # Spatial retriever selector
    sel = Spatial_RetrieverSelector(np.array([mapper_array]*2).T)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Spatial_RetrieverSelector(mapper_array, mapper_array)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Spatial_RetrieverSelector(mapper_function1)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Spatial_RetrieverSelector(mapper_function, mapper_function)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Spatial_RetrieverSelector(sel)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Spatial_RetrieverSelector((0, 0))
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Spatial_RetrieverSelector(0, 0)
    sel[0], sel[0, 1], sel[[0, 1]]
    try:
        ## Different types of core mappers
        boolean = False
        Spatial_RetrieverSelector(mapper_array, mapper_function)
        boolean = True
        raise Exception("It has to halt here.")
    except:
        if boolean:
            raise Exception("The test has to halt here.")
    try:
        ## Not correct shape
        boolean = False
        Spatial_RetrieverSelector(mapper_array)
        boolean = True
        raise Exception("It has to halt here.")
    except:
        if boolean:
            raise Exception("The test has to halt here.")

    # FeatureInd retriever selector
    sel = FeatInd_RetrieverSelector(np.array([mapper_array]*2).T)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = FeatInd_RetrieverSelector(mapper_array, mapper_array)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = FeatInd_RetrieverSelector(mapper_function1)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = FeatInd_RetrieverSelector(mapper_function, mapper_function)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = FeatInd_RetrieverSelector(sel)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = FeatInd_RetrieverSelector((0, 0))
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = FeatInd_RetrieverSelector(0, 0)
    sel[0], sel[0, 1], sel[[0, 1]]
    try:
        ## Different types of core mappers
        boolean = False
        FeatInd_RetrieverSelector(mapper_array, mapper_function)
        boolean = True
        raise Exception("It has to halt here.")
    except:
        if boolean:
            raise Exception("The test has to halt here.")
    try:
        ## Different types of core mappers
        boolean = False
        FeatInd_RetrieverSelector(np.array([mapper_array]*10).T)
        boolean = True
        raise Exception("It has to halt here.")
    except:
        if boolean:
            raise Exception("The test has to halt here.")

    # FeatureInd retriever selector
    sel = Desc_RetrieverSelector(np.array([mapper_array]*2).T)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Desc_RetrieverSelector(mapper_array, mapper_array)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Desc_RetrieverSelector(mapper_function1)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Desc_RetrieverSelector(mapper_function, mapper_function)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Desc_RetrieverSelector(sel)
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Desc_RetrieverSelector((0, 0))
    sel[0], sel[0, 1], sel[[0, 1]]
    sel = Desc_RetrieverSelector(0, 0)
    sel[0], sel[0, 1], sel[[0, 1]]
    try:
        ## Different types of core mappers
        boolean = False
        Desc_RetrieverSelector(mapper_array, mapper_function)
        boolean = True
        raise Exception("It has to halt here.")
    except:
        if boolean:
            raise Exception("The test has to halt here.")
    try:
        ## Different types of core mappers
        boolean = False
        Desc_RetrieverSelector(np.array([mapper_array]*10).T)
        boolean = True
        raise Exception("It has to halt here.")
    except:
        if boolean:
            raise Exception("The test has to halt here.")

    # FeatureInd retriever selector
    pos_selt = [(np.array([mapper_array]*2).T, ), (mapper_array, mapper_array),
                (mapper_function1, ), (mapper_function, mapper_function)]

    def test_getitem(selector):
        selector[0]
        selector[0, 1]
        selector[[0, 1]]
        try:
            boolean = False
            selector[0.7]
            boolean = True
        except:
            if boolean:
                raise Exception("It has to halt here.")

    for i in range(len(pos_selt)):
        ## Instantiation
        sel0 = Spatial_RetrieverSelector(*pos_selt[i])
        sel1 = FeatInd_RetrieverSelector(*pos_selt[i])
        sel2 = FeatInd_RetrieverSelector(*pos_selt[i])
        sel3 = Desc_RetrieverSelector(*pos_selt[i])
        selfeat = Feat_RetrieverSelector(sel1, sel2, sel3)
        test_getitem(selfeat)
        ## Partial information instantiation
        selfeat = Feat_RetrieverSelector(selfeat, None, None)
        test_getitem(selfeat)
        ### Testing Sp_DescriptorSelector
        sel = Sp_DescriptorSelector(sel0, selfeat)
        test_getitem(sel)
        ### Testing Sp_DescriptorSelector with partial instantiation
        sel = Sp_DescriptorSelector(sel)
        test_getitem(sel)

    #### Individual tests
    ## Partial information instantiation
    selfeat = Feat_RetrieverSelector((0, 0, 0, 0, 0, 0), None, None)
    # Getitem
    test_getitem(selfeat)
#    ## Partial information instantiation
    ## Instantiation
    sel1 = FeatInd_RetrieverSelector(sel1)
    sel2 = FeatInd_RetrieverSelector(sel2)
    sel3 = Desc_RetrieverSelector(sel3)
    selfeat = Feat_RetrieverSelector(sel1, sel2, sel3)
    test_getitem(selfeat)
    selfeat = Feat_RetrieverSelector(np.zeros((100, 6)))
    test_getitem(selfeat)
    selfeat = Feat_RetrieverSelector((lambda idx: (0, 0, 0, 0, 0, 0),
                                     {'n_in': 200}))
    test_getitem(selfeat)
def test():
    n = 100
    locs = np.random.random((n, 2))*100
    locs1 = random_transformed_space_points(n/10, 2, None)*100
    feats0 = np.random.random((n/10, 4))
    feats1 = np.random.random(n/10)
    kneighs4 = np.ones(len(locs)).astype(int)*4
    ret = KRetriever(locs1, 4, ifdistance=True)
    ret1 = KRetriever(locs1, ifdistance=True)

    f_dens0, f_dens1, f_dens2 = 'weighted_count', 'weighted_avg', 'null'
    pars_d = {}
    f_weights = ['null', 'linear', 'Trapezoid', 'inverse_prop', 'inverse_prop',
                 'exponential', 'exponential', 'gaussian', 'gaussian',
                 'surgaussian', 'surgaussian', 'surgaussian', 'sigmoid',
                 'sigmoid']

    pars_w1 = {'max_r': 0.2, 'max_w': 1, 'min_w': 0}
    pars_w2 = {'max_r': 0.2, 'r2': 0.4, 'max_w': 1, 'min_w': 0}

    pars_w3 = {'max_r': 0.2, 'max_w': 1, 'min_w': 1e-8, 'rescale': True}
    pars_w3b = {'max_r': 0.2, 'max_w': 1, 'min_w': 1e-8, 'rescale': False}
    pars_w5 = {'max_r': 0.2, 'max_w': 1, 'min_w': 0, 'S': None,
               'rescale': True}
    pars_w5b = {'max_r': 0.2, 'max_w': 1, 'min_w': 1e-3, 'S': None,
                'rescale': False}
    pars_w6 = {'max_r': 0.2, 'max_w': 1, 'min_w': 1e-3, 'S': 0.5,
               'rescale': True}
    pars_w7 = {'max_r': 0.2, 'max_w': 1, 'min_w': 1e-3, 'r_char': 0, 'B': None,
               'rescale': True}
    pars_w7b = {'max_r': 0.2, 'max_w': 1, 'min_w': 1e-3, 'r_char': 0, 'B': 2,
                'rescale': False}
    pars_w = [{}, pars_w1, pars_w2, pars_w3, pars_w3b, pars_w3, pars_w3b,
              pars_w5, pars_w5b, pars_w5, pars_w5b, pars_w6, pars_w7, pars_w7b]

    values, dists = np.random.random((10, 4)), np.random.random(10)

    pars_now = {'max_r': 0.2, 'max_w': 1, 'min_w': 1e-3, 'r_char': 0}
    set_scales_kernel('surgaussian', **pars_now)
    set_scales_kernel('gaussian', **pars_now)
    set_scales_kernel('sigmoid', **pars_now)

    for i in range(len(f_weights)):
        f = create_weighted_function(f_weights[i], pars_w[i], f_dens0, pars_d)
        f(values, dists)
        f = create_weighted_function(f_weights[i], pars_w[i], f_dens1, pars_d)
        f(values, dists)
        f = create_weighted_function(f_weights[i], pars_w[i],
                                     characterizer_summer, pars_d)
        f(values, dists)

    M = general_density_assignation(locs, ret, kneighs4, feats0,
                                    f_weights[1], pars_w[1], f_dens1, pars_d)
    M = general_density_assignation(locs, ret, kneighs4, feats1,
                                    f_weights[1], pars_w[1], f_dens1, pars_d)
    M = general_density_assignation(locs, ret1, kneighs4, feats1,
                                    f_weights[1], pars_w[1], f_dens1, pars_d)
    M = general_density_assignation(locs, ret1, kneighs4, feats1,
                                    f_weights[1], pars_w[1], f_dens0, pars_d)

    interpolator = Interpolator(f_weights[1], pars_w[1], f_dens1, pars_d)

    ###########################################################################
    ###########################################################################
    #### Density assignation testing
    ################################
    logfile = Logger('logfile.log')
    pars_dens_asign = {'f_weights': f_weights[1], 'params_w': pars_w[1],
                       'f_dens': f_dens1, 'params_d': pars_d}
    locs_data = locs
    pop_data = feats1
    retriever = KRetriever
    dens_asign = DensityAssign_Process(logfile, retriever)
    dens_asign.compute_density(locs1, locs_data, pop_data, kneighs4,
                               pars_dens_asign)
    os.remove('logfile.log')

    ###########################################################################
    ###########################################################################
    #### Interpolation utils testing
    ################################
    density0 = np.random.random(50)*2
    density1 = np.random.random(50)*2
    clustering_by_comparison(density0, density1)