def load_mini(N=1000):
    X, y = make_moons(N, noise=0.035, random_state=20)
    x_, y_ = make_circles(N, noise=0.02, random_state=20)
    x_[:, 1] += 2.0
    y_ += 2
    X = np.concatenate([X, x_], axis=0)
    y = np.concatenate([y, y_])
    X -= X.mean(0, keepdims=True)
    X /= X.max(0, keepdims=True)

    X = X.astype("float32")
    y = y.astype("int32")

    dict_init = [
        ("datum_shape", (2,)),
        ("n_classes", 4),
        ("name", "mini"),
        ("classes", [str(u) for u in range(4)]),
    ]

    dataset = Dataset(**dict(dict_init))
    dataset["inputs/train_set"] = X
    dataset["outputs/train_set"] = y

    return dataset 

def test_random_trees_dense_equal():
    # Test that the `sparse_output` parameter of RandomTreesEmbedding
    # works by returning the same array for both argument values.

    # Create the RTEs
    hasher_dense = RandomTreesEmbedding(n_estimators=10, sparse_output=False,
                                        random_state=0)
    hasher_sparse = RandomTreesEmbedding(n_estimators=10, sparse_output=True,
                                         random_state=0)
    X, y = datasets.make_circles(factor=0.5)
    X_transformed_dense = hasher_dense.fit_transform(X)
    X_transformed_sparse = hasher_sparse.fit_transform(X)

    # Assert that dense and sparse hashers have same array.
    assert_array_equal(X_transformed_sparse.toarray(), X_transformed_dense)


# Ignore warnings from switching to more power iterations in randomized_svd 

def test_random_hasher():
    # test random forest hashing on circles dataset
    # make sure that it is linearly separable.
    # even after projected to two SVD dimensions
    # Note: Not all random_states produce perfect results.
    hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
    X, y = datasets.make_circles(factor=0.5)
    X_transformed = hasher.fit_transform(X)

    # test fit and transform:
    hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
    assert_array_equal(hasher.fit(X).transform(X).toarray(),
                       X_transformed.toarray())

    # one leaf active per data point per forest
    assert_equal(X_transformed.shape[0], X.shape[0])
    assert_array_equal(X_transformed.sum(axis=1), hasher.n_estimators)
    svd = TruncatedSVD(n_components=2)
    X_reduced = svd.fit_transform(X_transformed)
    linear_clf = LinearSVC()
    linear_clf.fit(X_reduced, y)
    assert_equal(linear_clf.score(X_reduced, y), 1.) 

def test_make_circles():
    factor = 0.3

    for (n_samples, n_outer, n_inner) in [(7, 3, 4), (8, 4, 4)]:
        # Testing odd and even case, because in the past make_circles always
        # created an even number of samples.
        X, y = make_circles(n_samples, shuffle=False, noise=None,
                            factor=factor)
        assert_equal(X.shape, (n_samples, 2), "X shape mismatch")
        assert_equal(y.shape, (n_samples,), "y shape mismatch")
        center = [0.0, 0.0]
        for x, label in zip(X, y):
            dist_sqr = ((x - center) ** 2).sum()
            dist_exp = 1.0 if label == 0 else factor**2
            assert_almost_equal(dist_sqr, dist_exp,
                                err_msg="Point is not on expected circle")

        assert_equal(X[y == 0].shape, (n_outer, 2),
                     "Samples not correctly distributed across circles.")
        assert_equal(X[y == 1].shape, (n_inner, 2),
                     "Samples not correctly distributed across circles.")

    assert_raises(ValueError, make_circles, factor=-0.01)
    assert_raises(ValueError, make_circles, factor=1.) 

def test_gridsearch_pipeline_precomputed():
    # Test if we can do a grid-search to find parameters to separate
    # circles with a perceptron model using a precomputed kernel.
    X, y = make_circles(n_samples=400, factor=.3, noise=.05,
                        random_state=0)
    kpca = KernelPCA(kernel="precomputed", n_components=2)
    pipeline = Pipeline([("kernel_pca", kpca),
                         ("Perceptron", Perceptron(max_iter=5))])
    param_grid = dict(Perceptron__max_iter=np.arange(1, 5))
    grid_search = GridSearchCV(pipeline, cv=3, param_grid=param_grid)
    X_kernel = rbf_kernel(X, gamma=2.)
    grid_search.fit(X_kernel, y)
    assert_equal(grid_search.best_score_, 1)


# 0.23. warning about tol not having its correct default value. 

def test_nested_circles():
    # Test the linear separability of the first 2D KPCA transform
    X, y = make_circles(n_samples=400, factor=.3, noise=.05,
                        random_state=0)

    # 2D nested circles are not linearly separable
    train_score = Perceptron(max_iter=5).fit(X, y).score(X, y)
    assert_less(train_score, 0.8)

    # Project the circles data into the first 2 components of a RBF Kernel
    # PCA model.
    # Note that the gamma value is data dependent. If this test breaks
    # and the gamma value has to be updated, the Kernel PCA example will
    # have to be updated too.
    kpca = KernelPCA(kernel="rbf", n_components=2,
                     fit_inverse_transform=True, gamma=2.)
    X_kpca = kpca.fit_transform(X)

    # The data is perfectly linearly separable in that space
    train_score = Perceptron(max_iter=5).fit(X_kpca, y).score(X_kpca, y)
    assert_equal(train_score, 1.0) 

def test_rbf_kernel(ax, cost):
    train_x, train_y = make_circles(
        n_samples=500, noise=0.1, factor=0.1, random_state=1
    )
    train_y[train_y == 0] = -1
    scaler = StandardScaler()
    train_x_scaled = scaler.fit_transform(train_x, train_y)
    train_data = np.hstack((train_y.reshape(500, 1), train_x_scaled))
    mykernel = Kernel(kernel="rbf", degree=5, coef0=1, gamma=0.5)
    mysvm = SmoSVM(
        train=train_data,
        kernel_func=mykernel,
        cost=cost,
        tolerance=0.001,
        auto_norm=False,
    )
    mysvm.fit()
    plot_partition_boundary(mysvm, train_data, ax=ax) 

def generateData(n):
    """
    """
    np.random.seed(12046)
    blobs = make_blobs(n_samples=n, centers = [[-2, -2], [2, 2]])
    circles = make_circles(n_samples=n, factor=.4, noise=.05)
    moons = make_moons(n_samples=n, noise=.05)
    blocks = np.random.rand(n, 2) - 0.5
    y = (blocks[:, 0] * blocks[:, 1] < 0) + 0
    blocks = (blocks, y)
    # 由于神经网络对数据的线性变换不稳定，因此将数据做归一化处理
    scaler = StandardScaler()
    blobs = (scaler.fit_transform(blobs[0]), blobs[1])
    circles = (scaler.fit_transform(circles[0]), circles[1])
    moons = (scaler.fit_transform(moons[0]), moons[1])
    blocks = (scaler.fit_transform(blocks[0]), blocks[1])
    return blobs, circles, moons, blocks 

def test_random_trees_dense_equal():
    # Test that the `sparse_output` parameter of RandomTreesEmbedding
    # works by returning the same array for both argument values.

    # Create the RTEs
    hasher_dense = RandomTreesEmbedding(n_estimators=10, sparse_output=False,
                                        random_state=0)
    hasher_sparse = RandomTreesEmbedding(n_estimators=10, sparse_output=True,
                                         random_state=0)
    X, y = datasets.make_circles(factor=0.5)
    X_transformed_dense = hasher_dense.fit_transform(X)
    X_transformed_sparse = hasher_sparse.fit_transform(X)

    # Assert that dense and sparse hashers have same array.
    assert_array_equal(X_transformed_sparse.toarray(), X_transformed_dense)


# Ignore warnings from switching to more power iterations in randomized_svd 

def test_random_hasher():
    # test random forest hashing on circles dataset
    # make sure that it is linearly separable.
    # even after projected to two SVD dimensions
    # Note: Not all random_states produce perfect results.
    hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
    X, y = datasets.make_circles(factor=0.5)
    X_transformed = hasher.fit_transform(X)

    # test fit and transform:
    hasher = RandomTreesEmbedding(n_estimators=30, random_state=1)
    assert_array_equal(hasher.fit(X).transform(X).toarray(),
                       X_transformed.toarray())

    # one leaf active per data point per forest
    assert_equal(X_transformed.shape[0], X.shape[0])
    assert_array_equal(X_transformed.sum(axis=1), hasher.n_estimators)
    svd = TruncatedSVD(n_components=2)
    X_reduced = svd.fit_transform(X_transformed)
    linear_clf = LinearSVC()
    linear_clf.fit(X_reduced, y)
    assert_equal(linear_clf.score(X_reduced, y), 1.) 

def test_random_trees_dense_type():
    # Test that the `sparse_output` parameter of RandomTreesEmbedding
    # works by returning a dense array.

    # Create the RTE with sparse=False
    hasher = RandomTreesEmbedding(n_estimators=10, sparse_output=False)
    X, y = datasets.make_circles(factor=0.5)
    X_transformed = hasher.fit_transform(X)

    # Assert that type is ndarray, not scipy.sparse.csr.csr_matrix
    assert_equal(type(X_transformed), np.ndarray) 

def test_single_linkage_clustering():
    # Check that we get the correct result in two emblematic cases
    moons, moon_labels = make_moons(noise=0.05, random_state=42)
    clustering = AgglomerativeClustering(n_clusters=2, linkage='single')
    clustering.fit(moons)
    assert_almost_equal(normalized_mutual_info_score(clustering.labels_,
                                                     moon_labels), 1)

    circles, circle_labels = make_circles(factor=0.5, noise=0.025,
                                          random_state=42)
    clustering = AgglomerativeClustering(n_clusters=2, linkage='single')
    clustering.fit(circles)
    assert_almost_equal(normalized_mutual_info_score(clustering.labels_,
                                                     circle_labels), 1) 

def test_format_mapper_data(self, jinja_env):
        mapper = KeplerMapper()
        data, labels = make_circles(1000, random_state=0)
        lens = mapper.fit_transform(data, projection=[0])
        graph = mapper.map(lens, data)

        color_function = lens[:, 0]
        inverse_X = data
        projected_X = lens
        projected_X_names = ["projected_%s" % (i) for i in range(projected_X.shape[1])]
        inverse_X_names = ["inverse_%s" % (i) for i in range(inverse_X.shape[1])]
        custom_tooltips = np.array(["customized_%s" % (l) for l in labels])

        graph_data = format_mapper_data(
            graph,
            color_function,
            inverse_X,
            inverse_X_names,
            projected_X,
            projected_X_names,
            custom_tooltips,
            jinja_env,
        )
        # print(graph_data)
        # Dump to json so we can easily tell what's in it.
        graph_data = json.dumps(graph_data)

        # TODO test more properties!
        assert "name" in graph_data
        assert """cube2_cluster0""" in graph_data
        assert """projected_0""" in graph_data
        assert """inverse_0""" in graph_data

        assert """customized_""" in graph_data 

def test_complete_pipeline(self, CoverClass):
        # TODO: add a mock that asserts the cover was called appropriately.. or test number of cubes etc.
        data, _ = datasets.make_circles()

        data = data.astype(np.float64)
        mapper = KeplerMapper()
        graph = mapper.map(data, cover=CoverClass())
        mapper.visualize(graph) 

def make_two_rings(num_samples):
    samples, labels = make_circles(num_samples, shuffle=True, noise=None, random_state=None, factor=0.6)
    return samples 

def generate_data(n_samples, dataset, noise):
    if dataset == 'moons':
        return datasets.make_moons(
            n_samples=n_samples,
            noise=noise,
            random_state=0
        )

    elif dataset == 'circles':
        return datasets.make_circles(
            n_samples=n_samples,
            noise=noise,
            factor=0.5,
            random_state=1
        )

    elif dataset == 'linear':
        X, y = datasets.make_classification(
            n_samples=n_samples,
            n_features=2,
            n_redundant=0,
            n_informative=2,
            random_state=2,
            n_clusters_per_class=1
        )

        rng = np.random.RandomState(2)
        X += noise * rng.uniform(size=X.shape)
        linearly_separable = (X, y)

        return linearly_separable

    else:
        raise ValueError(
            'Data type incorrectly specified. Please choose an existing '
            'dataset.') 

def rand_ring2d(batch_size):
    """ This function generates 2D samples from a hollowed-cirlce distribution in a 2-dimensional space.

        Args:
            batch_size (int): number of batch samples

        Return:
            torch.Tensor: tensor of size (batch_size, 2)
    """
    circles = make_circles(2 * batch_size, noise=.01)
    z = np.squeeze(circles[0][np.argwhere(circles[1] == 0), :])
    return torch.from_numpy(z).type(torch.FloatTensor) 

def test_n_clusters_circles(self):
        """ Tests that DBSCAN finds the correct number of clusters with
        circle data.
        """
        n_samples = 1500
        x, y = make_circles(n_samples=n_samples, factor=.5, noise=.05)
        dbscan = DBSCAN(n_regions=1, eps=.15)
        x = StandardScaler().fit_transform(x)
        ds_x = ds.array(x, block_size=(300, 2))
        dbscan.fit(ds_x)
        self.assertEqual(dbscan.n_clusters, 2) 

def test_n_clusters_circles_max_samples(self):
        """ Tests that DBSCAN finds the correct number of clusters when
        defining max_samples with circle data.
        """
        n_samples = 1500
        x, y = make_circles(n_samples=n_samples, factor=.5, noise=.05)
        dbscan = DBSCAN(n_regions=1, eps=.15, max_samples=500)
        x = StandardScaler().fit_transform(x)
        ds_x = ds.array(x, block_size=(300, 2))
        dbscan.fit(ds_x)
        self.assertEqual(dbscan.n_clusters, 2) 

def test_n_clusters_circles_grid(self):
        """ Tests that DBSCAN finds the correct number of clusters when
        setting n_regions > 1 with circle data.
        """
        n_samples = 1500
        x, y = make_circles(n_samples=n_samples, factor=.5, noise=.05)
        dbscan = DBSCAN(n_regions=4, eps=.15, max_samples=700)
        x = StandardScaler().fit_transform(x)
        ds_x = ds.array(x, block_size=(300, 2))
        dbscan.fit(ds_x)
        self.assertEqual(dbscan.n_clusters, 2) 

def generateCircles(n):
    """
    生成圆圈数据
    """
    data, _ = make_circles(n_samples=n, factor=0.5, noise=0.06)
    return data 

def test_random_trees_dense_type():
    # Test that the `sparse_output` parameter of RandomTreesEmbedding
    # works by returning a dense array.

    # Create the RTE with sparse=False
    hasher = RandomTreesEmbedding(n_estimators=10, sparse_output=False)
    X, y = datasets.make_circles(factor=0.5)
    X_transformed = hasher.fit_transform(X)

    # Assert that type is ndarray, not scipy.sparse.csr.csr_matrix
    assert_equal(type(X_transformed), np.ndarray) 

def test_gridsearch_pipeline():
    # Test if we can do a grid-search to find parameters to separate
    # circles with a perceptron model.
    X, y = make_circles(n_samples=400, factor=.3, noise=.05,
                        random_state=0)
    kpca = KernelPCA(kernel="rbf", n_components=2)
    pipeline = Pipeline([("kernel_pca", kpca),
                         ("Perceptron", Perceptron(max_iter=5))])
    param_grid = dict(kernel_pca__gamma=2. ** np.arange(-2, 2))
    grid_search = GridSearchCV(pipeline, cv=3, param_grid=param_grid)
    grid_search.fit(X, y)
    assert_equal(grid_search.best_score_, 1) 

def test_gridsearch_pipeline_precomputed():
    # Test if we can do a grid-search to find parameters to separate
    # circles with a perceptron model using a precomputed kernel.
    X, y = make_circles(n_samples=400, factor=.3, noise=.05,
                        random_state=0)
    kpca = KernelPCA(kernel="precomputed", n_components=2)
    pipeline = Pipeline([("kernel_pca", kpca),
                         ("Perceptron", Perceptron(max_iter=5))])
    param_grid = dict(Perceptron__max_iter=np.arange(1, 5))
    grid_search = GridSearchCV(pipeline, cv=3, param_grid=param_grid)
    X_kernel = rbf_kernel(X, gamma=2.)
    grid_search.fit(X_kernel, y)
    assert_equal(grid_search.best_score_, 1) 

def cluster_data():
    np.random.seed(0)

    # ============
    # Generate datasets. We choose the size big enough to see the scalability
    # of the algorithms, but not too big to avoid too long running times
    # ============
    n_samples = 1500
    noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,
                                          noise=.05)
    noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)
    blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)

    # Anisotropicly distributed data
    random_state = 170
    X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)
    transformation = [[0.6, -0.6], [-0.4, 0.8]]
    X_aniso = np.dot(X, transformation)
    aniso = (X_aniso, y)

    # blobs with varied variances
    varied = datasets.make_blobs(n_samples=n_samples,
                                 cluster_std=[1.0, 2.5, 0.5],
                                 random_state=random_state)

    default_base = {'quantile': .3,
                    'eps': .3,
                    'damping': .9,
                    'preference': -200,
                    'n_neighbors': 10,
                    'n_clusters': 3,
                    'min_samples': 20,
                    'xi': 0.05,
                    'min_cluster_size': 0.1}

    data = [
        ('noisy_circles', noisy_circles, {'damping': .77, 'preference': -240,
                                          'quantile': .2, 'n_clusters': 2,
                                          'min_samples': 20, 'xi': 0.25}),
        ('noisy_moons', noisy_moons, {'damping': .75, 'preference': -220, 'n_clusters': 2}),
        ('varied', varied, {'eps': .18, 'n_neighbors': 2,
                            'min_samples': 5, 'xi': 0.035, 'min_cluster_size': .2}),
        ('aniso', aniso, {'eps': .15, 'n_neighbors': 2,
                          'min_samples': 20, 'xi': 0.1, 'min_cluster_size': .2}),
        ('blobs', blobs, {}),
    ]
    yield data, default_base 

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('--save', type=str, default='work')
    parser.add_argument('--nEpoch', type=int, default=100)
    # parser.add_argument('--testBatchSz', type=int, default=2048)
    parser.add_argument('--seed', type=int, default=42)
    parser.add_argument('--model', type=str, default="picnn",
                        choices=['picnn', 'ficnn'])
    parser.add_argument('--dataset', type=str, default="moons",
                        choices=['moons', 'circles', 'linear'])
    parser.add_argument('--noncvx', action='store_true')

    args = parser.parse_args()

    npr.seed(args.seed)
    tf.set_random_seed(args.seed)

    setproctitle.setproctitle('bamos.icnn.synthetic.{}.{}'.format(args.model, args.dataset))

    save = os.path.join(os.path.expanduser(args.save),
                        "{}.{}".format(args.model, args.dataset))
    if os.path.isdir(save):
        shutil.rmtree(save)
    os.makedirs(save, exist_ok=True)

    if args.dataset == "moons":
        (dataX, dataY) = make_moons(noise=0.3, random_state=0)
    elif args.dataset == "circles":
        (dataX, dataY) = make_circles(noise=0.2, factor=0.5, random_state=0)
        dataY = 1.-dataY
    elif args.dataset == "linear":
        (dataX, dataY) = make_classification(n_features=2, n_redundant=0, n_informative=2,
                                             random_state=1, n_clusters_per_class=1)
        rng = np.random.RandomState(2)
        dataX += 2 * rng.uniform(size=dataX.shape)
    else:
        assert(False)

    dataY = dataY.reshape((-1, 1)).astype(np.float32)

    nData = dataX.shape[0]
    nFeatures = dataX.shape[1]
    nLabels = 1
    nXy = nFeatures + nLabels

    config = tf.ConfigProto() #log_device_placement=False)
    config.gpu_options.allow_growth = True
    with tf.Session(config=config) as sess:
        model = Model(nFeatures, nLabels, sess, args.model, nGdIter=30)
        model.train(args, dataX, dataY)