Python distance.euclidean方法代码示例

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def eye_aspect_ratio(eye):
    # compute the euclidean distances between the two sets of
    # vertical eye landmarks (x, y)-coordinates
    A = dist.euclidean(eye[1], eye[5])
    B = dist.euclidean(eye[2], eye[4])

    # compute the euclidean distance between the horizontal
    # eye landmark (x, y)-coordinates
    C = dist.euclidean(eye[0], eye[3])

    # compute the eye aspect ratio
    ear = (A + B) / (2.0 * C)

    # return the eye aspect ratio
    return ear


# construct the argument parse and parse the arguments 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def _get_sorted_db_keypoint_distances(self, N=None):
        """Use a minimum spanning tree heuristic to find the N largest gaps in the
        line constituted by the current decision boundary keypoints.
        """
        if N == None:
            N = self.n_interpolated_keypoints
        edges = minimum_spanning_tree(
            squareform(pdist(self.decision_boundary_points_2d))
        )
        edged = np.array(
            [
                euclidean(
                    self.decision_boundary_points_2d[u],
                    self.decision_boundary_points_2d[v],
                )
                for u, v in edges
            ]
        )
        gap_edge_idx = np.argsort(edged)[::-1][: int(N)]
        edges = edges[gap_edge_idx]
        gap_distances = np.square(edged[gap_edge_idx])
        gap_probability_scores = gap_distances / np.sum(gap_distances)
        return edges, gap_distances, gap_probability_scores 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def setParameters(self, NP=43, alpha=(1, 0.83), gamma=(1.17, 0.56), theta=(0.932, 0.832), d=euclidean, dn=euclidean, nl=1, F=1.2, CR=0.25, Combination=Elitism, **ukwargs):
		r"""Set the parameters for the algorith.

		Arguments:
			alpha (Optional[List[float]]): Factor for fickleness index function :math:`\in [0, 1]`.
			gamma (Optional[List[float]]): Factor for external irregularity index function :math:`\in [0, \infty)`.
			theta (Optional[List[float]]): Factor for internal irregularity index function :math:`\in [0, \infty)`.
			d (Optional[Callable[[float, float], float]]): function that takes two arguments that are function values and calcs the distance between them.
			dn (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]): function that takes two arguments that are points in function landscape and calcs the distance between them.
			nl (Optional[float]): Normalized range for neighborhood search :math:`\in (0, 1]`.
			F (Optional[float]): Mutation parameter.
			CR (Optional[float]): Crossover parameter :math:`\in [0, 1]`.
			Combination (Optional[Callable[numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray, float, float, float, float, float, float, Task, mtrand.RandomState]]): Function for combining individuals to get new position/individual.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
			* Combination methods:
				* :func:`NiaPy.algorithms.other.Elitism`
				* :func:`NiaPy.algorithms.other.Crossover`
				* :func:`NiaPy.algorithms.other.Sequential`
		"""
		Algorithm.setParameters(self, NP=NP, **ukwargs)
		self.alpha, self.gamma, self.theta, self.d, self.dn, self.nl, self.F, self.CR, self.Combination = alpha, gamma, theta, d, dn, nl, F, CR, Combination 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def setParameters(self, N=25, phi=0.4, Fa=0.5, Fb=0.5, Fd=0.3, k=25, P_Cr=0.5, P_F=0.36, SexualCrossover=SexualCrossoverSimple, Brooding=BroodingSimple, Distance=euclidean, **ukwargs):
		r"""Set the parameters of the algorithm.

		Arguments:
			N (int): population size for population initialization.
			phi (int): TODO.
			Fa (float): Value $\in [0, 1]$ for Asexual reproduction size.
			Fb (float): Value $\in [0, 1]$ for Brooding size.
			Fd (float): Value $\in [0, 1]$ for Depredation size.
			k (int): Trys for larvae setting.
			SexualCrossover (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Crossover function.
			P_Cr (float): Crossover rate $\in [0, 1]$.
			Brooding (Callable[[numpy.ndarray, float, Task, mtrand.RandomState, Dict[str, Any]], Tuple[numpy.ndarray, numpy.ndarray]]): Brooding function.
			P_F (float): Crossover rate $\in [0, 1]$.
			Distance (Callable[[numpy.ndarray, numpy.ndarray], float]): Funciton for calculating distance between corals.

		See Also:
			* :func:`NiaPy.algorithms.Algorithm.setParameters`
		"""
		ukwargs.pop('NP', None)
		Algorithm.setParameters(self, NP=N, **ukwargs)
		self.phi, self.k, self.P_Cr, self.P_F = phi, k, P_Cr, P_F
		self.Fa, self.Fb, self.Fd = int(self.NP * Fa), int(self.NP * Fb), int(self.NP * Fd)
		self.SexualCrossover, self.Brooding, self.Distance = SexualCrossover, Brooding, Distance 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def setParameters(self, n=25, l0=5, nt=5, rho=0.4, gamma=0.6, beta=0.08, s=0.03, Distance=euclidean, **ukwargs):
		r"""Set the arguments of an algorithm.

		Arguments:
			n (Optional[int]): Number of glowworms in population.
			l0 (Optional[float]): Initial luciferin quantity for each glowworm.
			nt (Optional[float]): --
			rs (Optional]float]): Maximum sensing range.
			rho (Optional[float]): Luciferin decay constant.
			gamma (Optional[float]): Luciferin enhancement constant.
			beta (Optional[float]): --
			s (Optional[float]): --
			Distance (Optional[Callable[[numpy.ndarray, numpy.ndarray], float]]]): Measure distance between two individuals.
		"""
		ukwargs.pop('NP', None)
		Algorithm.setParameters(self, NP=n, **ukwargs)
		self.l0, self.nt, self.rho, self.gamma, self.beta, self.s, self.Distance = l0, nt, rho, gamma, beta, s, Distance 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def initPopulation(self, task):
		r"""Initialize population.

		Args:
			task (Task): Optimization task.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray[float], Dict[str, Any]]:
				1. Initialized population of glowwarms.
				2. Initialized populations function/fitness values.
				3. Additional arguments:
					* L (numpy.ndarray): TODO.
					* R (numpy.ndarray): TODO.
					* rs (numpy.ndarray): TODO.
		"""
		GS, GS_f, d = Algorithm.initPopulation(self, task)
		rs = euclidean(full(task.D, 0), task.bRange)
		L, R = full(self.NP, self.l0), full(self.NP, rs)
		d.update({'L': L, 'R': R, 'rs': rs})
		return GS, GS_f, d 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def selection(self, pop, npop, xb, fxb, task, **kwargs):
		r"""Operator for selection of individuals.

		Args:
			pop (numpy.ndarray): Current population.
			npop (numpy.ndarray): New population.
			xb (numpy.ndarray): Current global best solution.
			fxb (float): Current global best solutions fitness/objective value.
			task (Task): Optimization task.
			kwargs (Dict[str, Any]): Additional arguments.

		Returns:
			Tuple[numpy.ndarray, numpy.ndarray, float]:
				1. New population.
				2. New global best solution.
				3. New global best solutions fitness/objective value.
		"""
		P = []
		for e in npop:
			i = argmin([euclidean(e, f) for f in pop])
			P.append(pop[i] if pop[i].f < e.f else e)
		return asarray(P), xb, fxb 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def point_list_filter(
    point_list: typing.Sequence, distance: float, point_limit: int = None
) -> typing.Sequence:
    """ remove some points which are too close """
    if not point_limit:
        point_limit = 20

    point_list = sorted(list(set(point_list)), key=lambda o: o[0])
    new_point_list = [point_list[0]]
    for cur_point in point_list[1:]:
        for each_confirmed_point in new_point_list:
            cur_distance = euclidean(cur_point, each_confirmed_point)
            # existed
            if cur_distance < distance:
                break
        else:
            new_point_list.append(cur_point)
            if len(new_point_list) >= point_limit:
                break
    return new_point_list 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def compute_density_estimator(self, solutions: List[S]):
        solutions_size = len(solutions)
        if solutions_size <= self.k:
            return

        points = []
        for i in range(solutions_size):
            points.append(solutions[i].objectives)

        # Compute distance matrix
        self.distance_matrix = numpy.zeros(shape=(solutions_size, solutions_size))
        for i in range(solutions_size):
            for j in range(solutions_size):
                self.distance_matrix[i, j] = self.distance_matrix[j, i] = euclidean(solutions[i].objectives,
                                                                                    solutions[j].objectives)
        # Gets the k-nearest distance of all the solutions
        for i in range(solutions_size):
            distances = []
            for j in range(solutions_size):
                distances.append(self.distance_matrix[i, j])
            distances.sort()
            solutions[i].attributes['knn_density'] = distances[self.k] 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def word_mover_distance(first_sent_tokens, second_sent_tokens, wvmodel, distancefunc=euclidean, lpFile=None):
    """ Compute the Word Mover's distance (WMD) between the two given lists of tokens.

    Using methods of linear programming, supported by PuLP, calculate the WMD between two lists of words. A word-embedding
    model has to be provided. WMD is returned.

    Reference: Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, Kilian Q. Weinberger, "From Word Embeddings to Document Distances," *ICML* (2015).

    :param first_sent_tokens: first list of tokens.
    :param second_sent_tokens: second list of tokens.
    :param wvmodel: word-embedding models.
    :param distancefunc: distance function that takes two numpy ndarray.
    :param lpFile: log file to write out.
    :return: Word Mover's distance (WMD)
    :type first_sent_tokens: list
    :type second_sent_tokens: list
    :type wvmodel: gensim.models.keyedvectors.KeyedVectors
    :type distancefunc: function
    :type lpFile: str
    :rtype: float
    """
    prob = word_mover_distance_probspec(first_sent_tokens, second_sent_tokens, wvmodel,
                                        distancefunc=distancefunc, lpFile=lpFile)
    return pulp.value(prob.objective) 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def test_dbscan_feature():
    # Tests the DBSCAN algorithm with a feature vector array.
    # Parameters chosen specifically for this task.
    # Different eps to other test, because distance is not normalised.
    eps = 0.8
    min_samples = 10
    metric = 'euclidean'
    # Compute DBSCAN
    # parameters chosen for task
    core_samples, labels = dbscan(X, metric=metric, eps=eps,
                                  min_samples=min_samples)

    # number of clusters, ignoring noise if present
    n_clusters_1 = len(set(labels)) - int(-1 in labels)
    assert_equal(n_clusters_1, n_clusters)

    db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples)
    labels = db.fit(X).labels_

    n_clusters_2 = len(set(labels)) - int(-1 in labels)
    assert_equal(n_clusters_2, n_clusters) 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def test_dbscan_callable():
    # Tests the DBSCAN algorithm with a callable metric.
    # Parameters chosen specifically for this task.
    # Different eps to other test, because distance is not normalised.
    eps = 0.8
    min_samples = 10
    # metric is the function reference, not the string key.
    metric = distance.euclidean
    # Compute DBSCAN
    # parameters chosen for task
    core_samples, labels = dbscan(X, metric=metric, eps=eps,
                                  min_samples=min_samples,
                                  algorithm='ball_tree')

    # number of clusters, ignoring noise if present
    n_clusters_1 = len(set(labels)) - int(-1 in labels)
    assert_equal(n_clusters_1, n_clusters)

    db = DBSCAN(metric=metric, eps=eps, min_samples=min_samples,
                algorithm='ball_tree')
    labels = db.fit(X).labels_

    n_clusters_2 = len(set(labels)) - int(-1 in labels)
    assert_equal(n_clusters_2, n_clusters) 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def gap(data, refs=None, nrefs=20, ks=range(1,11), method=None):
    shape = data.shape
    if refs is None:
        tops = data.max(axis=0)
        bots = data.min(axis=0)
        dists = scipy.matrix(scipy.diag(tops-bots))

        rands = scipy.random.random_sample(size=(shape[0], shape[1], nrefs))
        for i in range(nrefs):
            rands[:, :, i] = rands[:, :, i]*dists+bots
    else:
        rands = refs
    gaps = scipy.zeros((len(ks),))
    for (i, k) in enumerate(ks):
        g1 = method(n_clusters=k).fit(data)
        (kmc, kml) = (g1.cluster_centers_, g1.labels_)
        disp = sum([euclidean(data[m, :], kmc[kml[m], :]) for m in range(shape[0])])

        refdisps = scipy.zeros((rands.shape[2],))
        for j in range(rands.shape[2]):
            g2 = method(n_clusters=k).fit(rands[:, :, j])
            (kmc, kml) = (g2.cluster_centers_, g2.labels_)
            refdisps[j] = sum([euclidean(rands[m, :, j], kmc[kml[m],:]) for m in range(shape[0])])
        gaps[i] = scipy.log(scipy.mean(refdisps))-scipy.log(disp)
    return gaps 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def get_ear(eye):

	# compute the euclidean distances between the two sets of
	# vertical eye landmarks (x, y)-coordinates
	A = dist.euclidean(eye[1], eye[5])
	B = dist.euclidean(eye[2], eye[4])
 
	# compute the euclidean distance between the horizontal
	# eye landmark (x, y)-coordinates
	C = dist.euclidean(eye[0], eye[3])
 
	# compute the eye aspect ratio
	ear = (A + B) / (2.0 * C)
 
	# return the eye aspect ratio
	return ear 

# 需要导入模块: from scipy.spatial import distance [as 别名]
# 或者: from scipy.spatial.distance import euclidean [as 别名]
def kMeansClustering(x,k):

    # Convert list into numpy format
    conv = np.asarray(x)

    # Compute the centroids
    centroids = kmeans(conv,k,iter=10)[0]

    # Relabel the x's
    labels = []
    for y in range(len(x)):
        minDist = float('inf')
        minLabel = -1
        for z in range(len(centroids)):
            e = euclidean(conv[y],centroids[z])
            if (e < minDist):
                minDist = e
                minLabel = z
        labels.append(minLabel)

    # Return the list of centroids and labels
    return (centroids,labels)

# Performs a weighted clustering on the examples in xTest
# Returns a 1-d vector of predictions