class DCS(object):

    @abstractmethod
    def select(self, ensemble, x):
        pass

    def __init__(self, Xval, yval, K=5, weighted=False, knn=None):
        self.Xval = Xval
        self.yval = yval
        self.K = K

        if knn == None:
            self.knn = KNeighborsClassifier(n_neighbors=K, algorithm='brute')
        else:
            self.knn = knn

        self.knn.fit(Xval, yval)
        self.weighted = weighted


    def get_neighbors(self, x, return_distance=False):
        # obtain the K nearest neighbors of test sample in the validation set
        if not return_distance:
            [idx] = self.knn.kneighbors(x, 
                    return_distance=return_distance)
        else:
            [dists], [idx] = self.knn.kneighbors(x, 
                    return_distance=return_distance)
        X_nn = self.Xval[idx] # k neighbors
        y_nn = self.yval[idx] # k neighbors target

        if return_distance:
            return X_nn, y_nn, dists
        else:
            return X_nn, y_nn

    def _pruning(self):

        if len(self.groups) < 2:
            return self.groups

        pruned, fst = False, True
        knn = KNeighborsClassifier(n_neighbors = 1, algorithm='brute')
        
        while pruned or fst:
            index = 0
            pruned, fst = False, False

            while index < len(self.groups):
                group = self.groups[index]

                mask = np.ones(len(self.groups), dtype=bool)
                mask[index] = False
                reps_x = np.asarray([g.rep_x for g in self.groups])[mask]
                reps_y = np.asarray([g.label for g in self.groups])[mask]
                labels = knn.fit(reps_x, reps_y).predict(group.X)

                if (labels == group.label).all():
                    self.groups.remove(group)
                    pruned = True
                else:
                    index = index + 1

                if len(self.groups) == 1:
                    index = len(self.groups)
                    pruned = False

        return self.groups

class RawModel:
    def __init__(self):
        # 2015-05-15 GEL Found that n_components=20 gives a nice balance of 
        # speed (substantial improvement), accuracy, and reduced memory usage 
        # (25% decrease).
        self.decomposer = TruncatedSVD(n_components=20)

        # 2015-05-15 GEL algorithm='ball_tree' uses less memory on average than 
        # algorithm='kd_tree'
        
        # 2015-05-15 GEL Evaluation of metrics by accuracy (based on 8000 training examples)
        # euclidean        0.950025
        # manhattan        0.933533
        # chebyshev        0.675662
        # hamming          0.708646
        # canberra         0.934033
        # braycurtis       0.940530
        self.model = KNeighborsClassifier(n_neighbors=5, algorithm='ball_tree', metric='euclidean')

    def fit(self, trainExamples):       
        X = self.decomposer.fit_transform( vstack( [reshape(x.X, (1, x.WIDTH * x.HEIGHT)) for x in trainExamples] ) )
        Y = [x.Y for x in trainExamples]

        self.model.fit(X, Y)
        return self

    def predict(self, examples):
        X = self.decomposer.transform( vstack( [reshape(x.X, (1, x.WIDTH * x.HEIGHT)) for x in examples] ) )
        return self.model.predict( X )

    def _main_loop(self):
        exit_count = 0
        knn = KNeighborsClassifier(n_neighbors = 1, algorithm='brute')
        while exit_count < len(self.groups):
            index, exit_count = 0, 0
            while index < len(self.groups):

                group = self.groups[index]
                reps_x = np.asarray([g.rep_x for g in self.groups])
                reps_y = np.asarray([g.label for g in self.groups])
                knn.fit(reps_x, reps_y)
                
                nn_idx = knn.kneighbors(group.X, n_neighbors=1, return_distance=False)
                nn_idx = nn_idx.T[0]
                mask = nn_idx == index
                
                # if all are correctly classified
                if not (False in mask):
                    exit_count = exit_count + 1
                
                # if all are misclasified
                elif not (group.label in reps_y[nn_idx]):
                    pca = PCA(n_components=1)
                    pca.fit(group.X)
                    # maybe use a 'for' instead of creating array
                    d = pca.transform(reps_x[index])
                    dis = [pca.transform(inst)[0] for inst in group.X]
                    mask_split = (dis < d).flatten()
                    
                    new_X = group.X[mask_split]
                    self.groups.append(_Group(new_X, group.label))
                    group.X = group.X[~mask_split]
                
                elif (reps_y[nn_idx] == group.label).all() and (nn_idx != index).any():
                    mask_mv = nn_idx != index
                    index_mv = np.asarray(range(len(group)))[mask_mv]
                    X_mv = group.remove_instances(index_mv)
                    G_mv = nn_idx[mask_mv]                        

                    for x, g in zip(X_mv, G_mv):
                        self.groups[g].add_instances([x])

                elif (reps_y[nn_idx] != group.label).sum()/float(len(group)) > self.r_mis:
                    mask_mv = reps_y[nn_idx] != group.label
                    new_X = group.X[mask_mv]
                    self.groups.append(_Group(new_X, group.label))
                    group.X = group.X[~mask_mv]
                else:
                   exit_count = exit_count + 1

                if len(group) == 0:
                    self.groups.remove(group)
                else:
                    index = index + 1

                for g in self.groups:
                    g.update_all()

        return self.groups                     

def evaluate(Xtra, ytra, Xtst, ytst, k=1, positive_label=1):
    knn = KNeighborsClassifier(n_neighbors=k, algorithm='brute')
    knn.fit(Xtra, ytra)

    y_true = ytst
    y_pred = knn.predict(Xtst)

    return evaluate_results(y_true, y_pred, positive_label=positive_label)

    def predict(self, X, n_neighbors=1):
        """Perform classification on an array of test vectors X.

        The predicted class C for each sample in X is returned.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]

        Returns
        -------
        C : array, shape = [n_samples]

        Notes
        -----
        The default prediction is using KNeighborsClassifier, if the
        instance reducition algorithm is to be performed with another
        classifier, it should be explicited overwritten and explained
        in the documentation.
        """
        X = atleast2d_or_csr(X)
        if not hasattr(self, "X_") or self.X_ is None:
            raise AttributeError("Model has not been trained yet.")

        if not hasattr(self, "y_") or self.y_ is None:
            raise AttributeError("Model has not been trained yet.")

        if self.classifier == None:
            self.classifier = KNeighborsClassifier(n_neighbors=n_neighbors)

        self.classifier.fit(self.X_, self.y_)
        return self.classifier.predict(X)

    def reduce_data(self, X, y):
        
        X, y = check_X_y(X, y, accept_sparse="csr")

        if self.classifier == None:
            self.classifier = KNeighborsClassifier(n_neighbors=self.n_neighbors)

        prots_s = []
        labels_s = []

        classes = np.unique(y)
        self.classes_ = classes

        for cur_class in classes:
            mask = y == cur_class
            insts = X[mask]
            prots_s = prots_s + [insts[np.random.randint(0, insts.shape[0])]]
            labels_s = labels_s + [cur_class]


        self.classifier.fit(prots_s, labels_s)
        for sample, label in zip(X, y):
            if self.classifier.predict(sample) != [label]:
                prots_s = prots_s + [sample]
                labels_s = labels_s + [label]
                self.classifier.fit(prots_s, labels_s)
       
        self.X_ = np.asarray(prots_s)
        self.y_ = np.asarray(labels_s)
        self.reduction_ = 1.0 - float(len(self.y_))/len(y)
        return self.X_, self.y_

    def reduce_data(self, X, y):
        X, y = check_X_y(X, y, accept_sparse="csr")

        if self.classifier == None:
            self.classifier = KNeighborsClassifier(n_neighbors=self.n_neighbors)
        if self.classifier.n_neighbors != self.n_neighbors:
            self.classifier.n_neighbors = self.n_neighbors

        classes = np.unique(y)
        self.classes_ = classes

        # loading inicial groups
        self.groups = []
        for label in classes:
            mask = y == label
            self.groups = self.groups + [_Group(X[mask], label)]

        self._main_loop()
        self._generalization_step()
        self._merge()
        self._pruning()
        self.X_ = np.asarray([g.rep_x for g in self.groups])
        self.y_ = np.asarray([g.label for g in self.groups])
        self.reduction_ = 1.0 - float(len(self.y_))/len(y)
        return self.X_, self.y_

    def reduce_data(self, X, y):
        if self.classifier == None:
            self.classifier = KNeighborsClassifier(n_neighbors=self.n_neighbors)
        if self.classifier.n_neighbors != self.n_neighbors:
            self.classifier.n_neighbors = self.n_neighbors

        X, y = check_arrays(X, y, sparse_format="csr")

        classes = np.unique(y)
        self.classes_ = classes

        if self.n_neighbors >= len(X):
            self.X_ = np.array(X)
            self.y_ = np.array(y)
            self.reduction_ = 0.0

        mask = np.zeros(y.size, dtype=bool)

        tmp_m = np.ones(y.size, dtype=bool)
        for i in xrange(y.size):
            tmp_m[i] = not tmp_m[i]
            self.classifier.fit(X[tmp_m], y[tmp_m])
            sample, label = X[i], y[i]

            if self.classifier.predict(sample) == [label]:
                mask[i] = not mask[i]

            tmp_m[i] = not tmp_m[i]

        self.X_ = np.asarray(X[mask])
        self.y_ = np.asarray(y[mask])
        self.reduction_ = 1.0 - float(len(self.y_)) / len(y)
        return self.X_, self.y_

    def index_nearest_neighbor(self, S, X, y):
        classifier = KNeighborsClassifier(n_neighbors=1)

        U = []
        S_mask = np.array(S, dtype=bool, copy=True)
        indexs = np.asarray(range(len(y)))[S_mask]
        X_tra, y_tra = X[S_mask], y[S_mask]

        for i in range(len(y)):
            real_indexes = np.asarray(range(len(y)))[S_mask]
            X_tra, y_tra = X[S_mask], y[S_mask]
            #print len(X_tra), len(y_tra)
            classifier.fit(X_tra, y_tra)
            [[index]] = classifier.kneighbors(X[i], return_distance=False)
            U = U + [real_indexes[index]]

        return U

    def __init__(self, Xval, yval, K=5, weighted=False, knn=None):
        self.Xval = Xval
        self.yval = yval
        self.K = K

        if knn == None:
            self.knn = KNeighborsClassifier(n_neighbors=K, algorithm='brute')
        else:
            self.knn = knn

        self.knn.fit(Xval, yval)
        self.weighted = weighted

def compute_cnn(X, y):

  "condenced nearest neighbor. the cnn removes reduntant instances, maintaining the samples in the decision boundaries."

  classifier = KNeighborsClassifier(n_neighbors=3)

  prots_s = []
  labels_s = []

  classes = np.unique(y)
  classes_ = classes

  for cur_class in classes:
    mask = y == cur_class
    insts = X[mask]
    prots_s = prots_s + [insts[np.random.randint(0, insts.shape[0])]]
    labels_s = labels_s + [cur_class]
    
  classifier.fit(prots_s, labels_s)
  for sample, label in zip(X, y):
    if classifier.predict(sample) != [label]:
      prots_s = prots_s + [sample]
      labels_s = labels_s + [label]
      classifier.fit(prots_s, labels_s)

  X_ = np.asarray(prots_s)
  y_ = np.asarray(labels_s)
  reduction_ = 1.0 - float(len(y_)/len(y))
  print reduction_

def compute_enn(X, y):
  """
  the edited nearest neighbors removes the instances in the boundaries, maintaining reduntant samples
  """

  classifier = KNeighborsClassifier(n_neighbors=3)

  classes = np.unique(y)
  classes_ = classes

  mask = np.zeros(y.size, dtype=bool)
  classifier.fit(X, y)

  for i in xrange(y.size):
    sample, label = X[i], y[i]
    if classifier.predict(sample) == [label]:
      mask[i] = not mask[i]

  X_ = np.asarray(X[mask])
  y_ = np.asarray(y[mask])
  reduction_ = 1.0 - float(len(y_)) / len(y)
  print reduction_

    def __init__(self, n_neighbors=1, alpha=0.6, max_loop=1000, threshold=0, chromosomes_count=10):
        self.n_neighbors = n_neighbors
        self.alpha = alpha
        self.max_loop = max_loop
        self.threshold = threshold
        self.chromosomes_count = chromosomes_count

        self.evaluations = None
        self.chromosomes = None

        self.best_chromosome_ac = -1
        self.best_chromosome_rd = -1

        self.classifier = KNeighborsClassifier(n_neighbors = n_neighbors)

    def reduce_data(self, X, y):
        if self.classifier == None:
            self.classifier = KNeighborsClassifier(n_neighbors=self.n_neighbors, algorithm='brute')
        if self.classifier.n_neighbors != self.n_neighbors:
            self.classifier.n_neighbors = self.n_neighbors

        X, y = check_arrays(X, y, sparse_format="csr")

        classes = np.unique(y)
        self.classes_ = classes
        self.classifier.fit(X, y)
        nn_idx = self.classifier.kneighbors(X, n_neighbors=2, return_distance=False)
        nn_idx = nn_idx.T[1]

        mask = [nn_idx[nn_idx[index]] == index and y[index] != y[nn_idx[index]] for index in xrange(nn_idx.shape[0])]
        mask = ~np.asarray(mask) 
        if self.keep_class != None and self.keep_class in self.classes_:
            mask[y==self.keep_class] = True

        self.X_ = np.asarray(X[mask])
        self.y_ = np.asarray(y[mask])
        self.reduction_ = 1.0 - float(len(self.y_)) / len(y)

        return self.X_, self.y_

class ENN(InstanceReductionMixin):

    """Edited Nearest Neighbors.

    The Edited Nearest Neighbors  removes the instances in de 
    boundaries, maintaining redudant samples.

    Parameters
    ----------
    n_neighbors : int, optional (default = 3)
        Number of neighbors to use by default for :meth:`k_neighbors` queries.

    Attributes
    ----------
    `X_` : array-like, shape = [indeterminated, n_features]
        Selected prototypes.

    `y_` : array-like, shape = [indeterminated]
        Labels of the selected prototypes.

    `reduction_` : float, percentual of reduction.

    Examples
    --------
    >>> from protopy.selection.enn import ENN
    >>> import numpy as np
    >>> X = np.array([[-1, 0], [-0.8, 1], [-0.8, -1], [-0.5, 0] , [0.5, 0], [1, 0], [0.8, 1], [0.8, -1]])
    >>> y = np.array([1, 1, 1, 2, 1, 2, 2, 2])
    >>> editednn = ENN()
    >>> editednn.fit(X, y)
    ENN(n_neighbors=3)
    >>> print(editednn.predict([[-0.6, 0.6]]))
    [1]
    >>> print editednn.reduction_
    0.75

    See also
    --------
    sklearn.neighbors.KNeighborsClassifier: nearest neighbors classifier

    References
    ----------
    Ruiqin Chang, Zheng Pei, and Chao Zhang. A modified editing k-nearest
    neighbor rule. JCP, 6(7):1493–1500, 2011.

    """

    def __init__(self, n_neighbors=3):
        self.n_neighbors = n_neighbors
        self.classifier = None


    def reduce_data(self, X, y):
        if self.classifier == None:
            self.classifier = KNeighborsClassifier(n_neighbors=self.n_neighbors)
        if self.classifier.n_neighbors != self.n_neighbors:
            self.classifier.n_neighbors = self.n_neighbors

        X, y = check_arrays(X, y, sparse_format="csr")

        classes = np.unique(y)
        self.classes_ = classes

        if self.n_neighbors >= len(X):
            self.X_ = np.array(X)
            self.y_ = np.array(y)
            self.reduction_ = 0.0

        mask = np.zeros(y.size, dtype=bool)

        tmp_m = np.ones(y.size, dtype=bool)
        for i in xrange(y.size):
            tmp_m[i] = not tmp_m[i]
            self.classifier.fit(X[tmp_m], y[tmp_m])
            sample, label = X[i], y[i]

            if self.classifier.predict(sample) == [label]:
                mask[i] = not mask[i]

            tmp_m[i] = not tmp_m[i]

        self.X_ = np.asarray(X[mask])
        self.y_ = np.asarray(y[mask])
        self.reduction_ = 1.0 - float(len(self.y_)) / len(y)
        return self.X_, self.y_

class SGP2(SGP):
    """Self-Generating Prototypes 2

    The Self-Generating Prototypes 2 is the second version of the
    Self-Generating Prototypes algorithm.
    It has a higher generalization power, including the procedures
    merge and pruning.

    Parameters
    ----------
    r_min: float, optional (default = 0.0)
        Determine the minimum size of a cluster [0.00, 0.20]

    r_mis: float, optional (default = 0.0)
        Determine the error tolerance before split a group

    Attributes
    ----------
    `X_` : array-like, shape = [indeterminated, n_features]
        Selected prototypes.

    `y_` : array-like, shape = [indeterminated]
        Labels of the selected prototypes.

    `reduction_` : float, percentual of reduction.

    Examples
    --------
    >>> from protopy.generation.sgp import SGP2
    >>> import numpy as np
    >>> X = [np.asarray(range(1,13)) + np.asarray([0.1,0,-0.1,0.1,0,-0.1,0.1,-0.1,0.1,-0.1,0.1,-0.1])]
    >>> X = np.asarray(X).T
    >>> y = np.array([1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 1])
    >>> sgp2 = SGP2()
    >>> sgp2.fit(X, y)
    SGP2(r_min=0.0, r_mis=0.0)
    >>> print sgp2.reduction_
    0.5

    See also
    --------
    protopy.generation.SGP: self-generating prototypes
    protopy.generation.sgp.ASGP: adaptive self-generating prototypes

    References
    ----------
    Hatem A. Fayed, Sherif R Hashem, and Amir F Atiya. Self-generating prototypes
    for pattern classification. Pattern Recognition, 40(5):1498–1509, 2007.
    """
    def __init__(self, r_min=0.0, r_mis=0.0):
        self.groups = None
        self.r_min = r_min
        self.r_mis = r_mis
        self.n_neighbors = 1
        self.classifier = None
        self.groups = None


    def reduce_data(self, X, y):
        X, y = check_X_y(X, y, accept_sparse="csr")

        if self.classifier == None:
            self.classifier = KNeighborsClassifier(n_neighbors=self.n_neighbors)
        if self.classifier.n_neighbors != self.n_neighbors:
            self.classifier.n_neighbors = self.n_neighbors

        classes = np.unique(y)
        self.classes_ = classes

        # loading inicial groups
        self.groups = []
        for label in classes:
            mask = y == label
            self.groups = self.groups + [_Group(X[mask], label)]

        self._main_loop()
        self._generalization_step()
        self._merge()
        self._pruning()
        self.X_ = np.asarray([g.rep_x for g in self.groups])
        self.y_ = np.asarray([g.label for g in self.groups])
        self.reduction_ = 1.0 - float(len(self.y_))/len(y)
        return self.X_, self.y_


    def _merge(self):

        if len(self.groups) < 2:
            return self.groups

        merged = False
        for group in self.groups:
            reps_x = np.asarray([g.rep_x for g in self.groups])
            reps_y = np.asarray([g.label for g in self.groups])
            self.classifier.fit(reps_x, reps_y)

            nn2_idx = self.classifier.kneighbors(group.X, n_neighbors=2, return_distance=False)
            nn2_idx = nn2_idx.T[1]

            # could use a threshold
#.........这里部分代码省略.........

def nearest_fit(X,y):
    clf = KNeighborsClassifier(7, 'distance')
    return clf.fit(X, y)

def knn_score(X, y, neighbors):
    knn5 = KNeighborsClassifier(n_neighbors=neighbors)
    knn5.fit(X, y)
    y_pred = knn5.predict(X)
    print "KNN{} accuracy_score: {}".format(neighbors,
                                            metrics.accuracy_score(y, y_pred))

class SSMA(InstanceReductionMixin):
    """Steady State Memetic Algorithm

    The Steady-State Memetic Algorithm is an evolutionary prototype
    selection algorithm. It uses a memetic algorithm in order to 
    perform a local search in the code.

    Parameters
    ----------
    n_neighbors : int, optional (default = 3)
        Number of neighbors to use by default for :meth:`k_neighbors` queries.

    alpha   : float (default = 0.6)
        Parameter that ponderates the fitness function.

    max_loop    : int (default = 1000)
        Number of maximum loops performed by the algorithm.

    threshold   : int (default = 0)
        Threshold that regulates the substitution condition;

    chromosomes_count: int (default = 10)
        number of chromosomes used to find the optimal solution.

    Attributes
    ----------
    `X_` : array-like, shape = [indeterminated, n_features]
        Selected prototypes.

    `y_` : array-like, shape = [indeterminated]
        Labels of the selected prototypes.

    `reduction_` : float, percentual of reduction.

    Examples
    --------
    >>> from protopy.selection.ssma import SSMA
    >>> import numpy as np
    >>> X = np.array([[i] for i in range(100)])
    >>> y = np.asarray(50 * [0] + 50 * [1])
    >>> ssma = SSMA()
    >>> ssma.fit(X, y)
    SSMA(alpha=0.6, chromosomes_count=10, max_loop=1000, threshold=0)
    >>> print ssma.predict([[40],[60]])
    [0 1]
    >>> print ssma.reduction_
    0.98

    See also
    --------
    sklearn.neighbors.KNeighborsClassifier: nearest neighbors classifier

    References
    ----------
    Joaquín Derrac, Salvador García, and Francisco Herrera. Stratified prototype
    selection based on a steady-state memetic algorithm: a study of scalability.
    Memetic Computing, 2(3):183–199, 2010.

    """
    def __init__(self, n_neighbors=1, alpha=0.6, max_loop=1000, threshold=0, chromosomes_count=10):
        self.n_neighbors = n_neighbors
        self.alpha = alpha
        self.max_loop = max_loop
        self.threshold = threshold
        self.chromosomes_count = chromosomes_count

        self.evaluations = None
        self.chromosomes = None

        self.best_chromosome_ac = -1
        self.best_chromosome_rd = -1

        self.classifier = KNeighborsClassifier(n_neighbors = n_neighbors)


    def accuracy(self, chromosome, X, y):
        mask = np.asarray(chromosome, dtype=bool)
        cX, cy = X[mask], y[mask]
        #print len(cX), len(cy), sum(chromosome)

        self.classifier.fit(cX, cy)
        labels = self.classifier.predict(X)
        accuracy = (labels == y).sum()

        return float(accuracy)/len(y)


    def fitness(self, chromosome, X, y):
        #TODO add the possibility of use AUC for factor1
        ac = self.accuracy(chromosome, X, y)
        rd = 1.0 - (float(sum(chromosome))/len(chromosome))

        return self.alpha * ac + (1.0 - self.alpha) * rd


    def fitness_gain(self, gain, n):
        return self.alpha * (float(gain)/n) + (1 - self.alpha) * (1.0 / n)


    def update_threshold(self, X, y):
#.........这里部分代码省略.........