Python KDTree.valid_metrics方法代码示例

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def app_entropy(x, order=2, metric='chebyshev'):
    """Approximate Entropy
    Parameters
    ----------
    x : list or np.array
        One-dimensional time series of shape (n_times)
    order : int (default: 2)
        Embedding dimension.
    metric : str (default: chebyshev)
        Name of the metric function used with
        :class:`~sklearn.neighbors.KDTree`. The list of available
        metric functions is given by: ``KDTree.valid_metrics``.
    Returns
    -------
    ae : float
        Approximate Entropy.
 
    """
    phi = _app_samp_entropy(x, order=order, metric=metric, approximate=True)
    return np.subtract(phi[0], phi[1]) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def sample_entropy(x, order=2, metric='chebyshev'):
    """Sample Entropy.
    Parameters
    ----------
    x : list or np.array
        One-dimensional time series of shape (n_times)
    order : int (default: 2)
        Embedding dimension.
    metric : str (default: chebyshev)
        Name of the metric function used with KDTree. The list of available
        metric functions is given by: `KDTree.valid_metrics`.
    Returns
    -------
    se : float
        Sample Entropy.

    """
    x = np.asarray(x, dtype=np.float64)
    if metric == 'chebyshev' and x.size < 5000:
        return _numba_sampen(x, mm=order, r=0.2)
    else:
        phi = _app_samp_entropy(x, order=order, metric=metric,
                                approximate=False) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def test_kde_algorithm_metric_choice():
    # Smoke test for various metrics and algorithms
    rng = np.random.RandomState(0)
    X = rng.randn(10, 2)    # 2 features required for haversine dist.
    Y = rng.randn(10, 2)

    for algorithm in ['auto', 'ball_tree', 'kd_tree']:
        for metric in ['euclidean', 'minkowski', 'manhattan',
                       'chebyshev', 'haversine']:
            if algorithm == 'kd_tree' and metric not in KDTree.valid_metrics:
                assert_raises(ValueError, KernelDensity,
                              algorithm=algorithm, metric=metric)
            else:
                kde = KernelDensity(algorithm=algorithm, metric=metric)
                kde.fit(X)
                y_dens = kde.score_samples(Y)
                assert_equal(y_dens.shape, Y.shape[:1]) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def entropy_app_samp_entropy(x, order, metric="chebyshev", approximate=True):
    _all_metrics = KDTree.valid_metrics
    if metric not in _all_metrics:
        raise ValueError(
            "The given metric (%s) is not valid. The valid " "metric names are: %s" % (metric, _all_metrics)
        )
    phi = np.zeros(2)
    r = 0.2 * np.std(x, axis=-1, ddof=1)

    # compute phi(order, r)
    _emb_data1 = entropy_embed(x, order, 1)
    if approximate:
        emb_data1 = _emb_data1
    else:
        emb_data1 = _emb_data1[:-1]
    count1 = KDTree(emb_data1, metric=metric).query_radius(emb_data1, r, count_only=True).astype(np.float64)
    # compute phi(order + 1, r)
    emb_data2 = entropy_embed(x, order + 1, 1)
    count2 = KDTree(emb_data2, metric=metric).query_radius(emb_data2, r, count_only=True).astype(np.float64)
    if approximate:
        phi[0] = np.mean(np.log(count1 / emb_data1.shape[0]))
        phi[1] = np.mean(np.log(count2 / emb_data2.shape[0]))
    else:
        phi[0] = np.mean((count1 - 1) / (emb_data1.shape[0] - 1))
        phi[1] = np.mean((count2 - 1) / (emb_data2.shape[0] - 1))
    return phi 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def _app_samp_entropy(x, order, metric='chebyshev', approximate=True):
    """Utility function for `app_entropy`` and `sample_entropy`.
    """
    _all_metrics = KDTree.valid_metrics
    if metric not in _all_metrics:
        raise ValueError('The given metric (%s) is not valid. The valid '
                         'metric names are: %s' % (metric, _all_metrics))
    phi = np.zeros(2)
    r = 0.2 * np.std(x, axis=-1, ddof=1)

    # compute phi(order, r)
    _emb_data1 = _embed(x, order, 1)
    if approximate:
        emb_data1 = _emb_data1
    else:
        emb_data1 = _emb_data1[:-1]
    count1 = KDTree(emb_data1, metric=metric).query_radius(emb_data1, r,
                                                           count_only=True
                                                           ).astype(np.float64)
    # compute phi(order + 1, r)
    emb_data2 = _embed(x, order + 1, 1)
    count2 = KDTree(emb_data2, metric=metric).query_radius(emb_data2, r,
                                                           count_only=True
                                                           ).astype(np.float64)
    if approximate:
        phi[0] = np.mean(np.log(count1 / emb_data1.shape[0]))
        phi[1] = np.mean(np.log(count2 / emb_data2.shape[0]))
    else:
        phi[0] = np.mean((count1 - 1) / (emb_data1.shape[0] - 1))
        phi[1] = np.mean((count2 - 1) / (emb_data2.shape[0] - 1))
    return phi


#The main code 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def __init__(self,
                 n_neighbors=5,
                 max_window_size=1000,
                 leaf_size=30,
                 metric='euclidean'):
        self.n_neighbors = n_neighbors
        self.max_window_size = max_window_size
        self.leaf_size = leaf_size
        if metric not in self.valid_metrics():
            raise ValueError("Invalid metric: {}.\n"
                             "Valid options are: {}".format(metric,
                                                            self.valid_metrics()))
        self.metric = metric
        self.data_window = SlidingWindow(window_size=max_window_size) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def valid_metrics():
        """ Get valid distance metrics for the KDTree. """
        return KDTree.valid_metrics 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def test_kde_algorithm_metric_choice(algorithm, metric):
    # Smoke test for various metrics and algorithms
    rng = np.random.RandomState(0)
    X = rng.randn(10, 2)    # 2 features required for haversine dist.
    Y = rng.randn(10, 2)

    if algorithm == 'kd_tree' and metric not in KDTree.valid_metrics:
        assert_raises(ValueError, KernelDensity,
                      algorithm=algorithm, metric=metric)
    else:
        kde = KernelDensity(algorithm=algorithm, metric=metric)
        kde.fit(X)
        y_dens = kde.score_samples(Y)
        assert_equal(y_dens.shape, Y.shape[:1]) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def compute_app_entropy(data, emb=2, metric='chebyshev'):
    """Approximate Entropy (AppEn, per channel).

    Parameters
    ----------
    data : ndarray, shape (n_channels, n_times)

    emb : int (default: 2)
        Embedding dimension.

    metric : str (default: chebyshev)
        Name of the metric function used with
        :class:`~sklearn.neighbors.KDTree`. The list of available
        metric functions is given by: ``KDTree.valid_metrics``.

    Returns
    -------
    output : ndarray, shape (n_channels,)

    Notes
    -----
    Alias of the feature function: **app_entropy**. See [1]_.

    References
    ----------
    .. [1] Richman, J. S. et al. (2000). Physiological time-series analysis
           using approximate entropy and sample entropy. American Journal of
           Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049.
    """
    phi = _app_samp_entropy_helper(data, emb=emb, metric=metric,
                                   approximate=True)
    return np.subtract(phi[:, 0], phi[:, 1]) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def compute_samp_entropy(data, emb=2, metric='chebyshev'):
    """Sample Entropy (SampEn, per channel).

    Parameters
    ----------
    data : ndarray, shape (n_channels, n_times)

    emb : int (default: 2)
        Embedding dimension.

    metric : str (default: chebyshev)
        Name of the metric function used with KDTree. The list of available
        metric functions is given by: `KDTree.valid_metrics`.

    Returns
    -------
    output : ndarray, shape (n_channels,)

    Notes
    -----
    Alias of the feature function: **samp_entropy**. See [1]_.

    References
    ----------
    .. [1] Richman, J. S. et al. (2000). Physiological time-series analysis
           using approximate entropy and sample entropy. American Journal of
           Physiology-Heart and Circulatory Physiology, 278(6), H2039-H2049.
    """
    phi = _app_samp_entropy_helper(data, emb=emb, metric=metric,
                                   approximate=False)
    if np.allclose(phi[:, 0], 0) or np.allclose(phi[:, 1], 0):
        raise ValueError('Sample Entropy is not defined.')
    else:
        return -np.log(np.divide(phi[:, 1], phi[:, 0])) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def _app_samp_entropy(x, order, metric='chebyshev', approximate=True):
    """Utility function for `app_entropy`` and `sample_entropy`.
    """
    _all_metrics = KDTree.valid_metrics
    if metric not in _all_metrics:
        raise ValueError('The given metric (%s) is not valid. The valid '
                         'metric names are: %s' % (metric, _all_metrics))
    phi = np.zeros(2)
    r = 0.2 * np.std(x, ddof=0)

    # compute phi(order, r)
    _emb_data1 = _embed(x, order, 1)
    if approximate:
        emb_data1 = _emb_data1
    else:
        emb_data1 = _emb_data1[:-1]
    count1 = KDTree(emb_data1, metric=metric).query_radius(emb_data1, r,
                                                           count_only=True
                                                           ).astype(np.float64)
    # compute phi(order + 1, r)
    emb_data2 = _embed(x, order + 1, 1)
    count2 = KDTree(emb_data2, metric=metric).query_radius(emb_data2, r,
                                                           count_only=True
                                                           ).astype(np.float64)
    if approximate:
        phi[0] = np.mean(np.log(count1 / emb_data1.shape[0]))
        phi[1] = np.mean(np.log(count2 / emb_data2.shape[0]))
    else:
        phi[0] = np.mean((count1 - 1) / (emb_data1.shape[0] - 1))
        phi[1] = np.mean((count2 - 1) / (emb_data2.shape[0] - 1))
    return phi 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def test_kde_sample_weights():
    n_samples = 400
    size_test = 20
    weights_neutral = np.full(n_samples, 3.)
    for d in [1, 2, 10]:
        rng = np.random.RandomState(0)
        X = rng.rand(n_samples, d)
        weights = 1 + (10 * X.sum(axis=1)).astype(np.int8)
        X_repetitions = np.repeat(X, weights, axis=0)
        n_samples_test = size_test // d
        test_points = rng.rand(n_samples_test, d)
        for algorithm in ['auto', 'ball_tree', 'kd_tree']:
            for metric in ['euclidean', 'minkowski', 'manhattan',
                           'chebyshev']:
                if algorithm != 'kd_tree' or metric in KDTree.valid_metrics:
                    kde = KernelDensity(algorithm=algorithm, metric=metric)

                    # Test that adding a constant sample weight has no effect
                    kde.fit(X, sample_weight=weights_neutral)
                    scores_const_weight = kde.score_samples(test_points)
                    sample_const_weight = kde.sample(random_state=1234)
                    kde.fit(X)
                    scores_no_weight = kde.score_samples(test_points)
                    sample_no_weight = kde.sample(random_state=1234)
                    assert_allclose(scores_const_weight, scores_no_weight)
                    assert_allclose(sample_const_weight, sample_no_weight)

                    # Test equivalence between sampling and (integer) weights
                    kde.fit(X, sample_weight=weights)
                    scores_weight = kde.score_samples(test_points)
                    sample_weight = kde.sample(random_state=1234)
                    kde.fit(X_repetitions)
                    scores_ref_sampling = kde.score_samples(test_points)
                    sample_ref_sampling = kde.sample(random_state=1234)
                    assert_allclose(scores_weight, scores_ref_sampling)
                    assert_allclose(sample_weight, sample_ref_sampling)

                    # Test that sample weights has a non-trivial effect
                    diff = np.max(np.abs(scores_no_weight - scores_weight))
                    assert diff > 0.001

                    # Test invariance with respect to arbitrary scaling
                    scale_factor = rng.rand()
                    kde.fit(X, sample_weight=(scale_factor * weights))
                    scores_scaled_weight = kde.score_samples(test_points)
                    assert_allclose(scores_scaled_weight, scores_weight) 

# 需要导入模块: from sklearn.neighbors import KDTree [as 别名]
# 或者: from sklearn.neighbors.KDTree import valid_metrics [as 别名]
def _app_samp_entropy_helper(data, emb, metric='chebyshev',
                             approximate=True):
    """Utility function for `compute_app_entropy`` and `compute_samp_entropy`.

    Parameters
    ----------
    data : ndarray, shape (n_channels, n_times)

    emb : int (default: 2)
        Embedding dimension.

    metric : str (default: chebyshev)
        Name of the metric function used with KDTree. The list of available
        metric functions is given by: ``KDTree.valid_metrics``.

    approximate : bool (default: True)
        If True, the returned values will be used to compute the
        Approximate Entropy (AppEn). Otherwise, the values are used to compute
        the Sample Entropy (SampEn).

    Returns
    -------
    output : ndarray, shape (n_channels, 2)
    """
    _all_metrics = KDTree.valid_metrics
    if metric not in _all_metrics:
        raise ValueError('The given metric (%s) is not valid. The valid '
                         'metric names are: %s' % (metric, _all_metrics))
    n_channels, n_times = data.shape
    phi = np.empty((n_channels, 2))
    for j in range(n_channels):
        r = 0.2 * np.std(data[j, :], axis=-1, ddof=1)
        # compute phi(emb, r)
        _emb_data1 = _embed(data[j, None], emb, 1)[0, :, :]
        if approximate:
            emb_data1 = _emb_data1
        else:
            emb_data1 = _emb_data1[:-1, :]
        count1 = KDTree(emb_data1, metric=metric).query_radius(
            emb_data1, r, count_only=True).astype(np.float64)
        # compute phi(emb + 1, r)
        emb_data2 = _embed(data[j, None], emb + 1, 1)[0, :, :]
        count2 = KDTree(emb_data2, metric=metric).query_radius(
            emb_data2, r, count_only=True).astype(np.float64)
        if approximate:
            phi[j, 0] = np.mean(np.log(count1 / emb_data1.shape[0]))
            phi[j, 1] = np.mean(np.log(count2 / emb_data2.shape[0]))
        else:
            phi[j, 0] = np.mean((count1 - 1) / (emb_data1.shape[0] - 1))
            phi[j, 1] = np.mean((count2 - 1) / (emb_data2.shape[0] - 1))
    return phi