コード例 #1
0
ファイル: test.py プロジェクト: vkhakham/k-segment
 def test_calc_best_fit_line_weighted(self):
     data = np.array([[1, 3.2627812, -3.1364346],
                      [2, 3.4707861, -3.28776192],
                      [3, 3.67879099, -3.43908923]])
     w = [1.0, 1.0, 1.0]
     best_fit_line = utils.calc_best_fit_line_polyfit(data, w)
     print best_fit_line
コード例 #2
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ファイル: ksegment.py プロジェクト: vkhakham/k-segment
def calc_prep_dist(P):
    prep_dist = np.full((len(P), len(P)), float("inf"))
    for index, value in np.ndenumerate(prep_dist):
        if index[0] < index[1]:
            segment = P[index[0] : index[1] + 1, :]
            best_fit_line = utils.calc_best_fit_line_polyfit(segment)
            prep_dist[index] = utils.sqrd_dist_sum(segment, best_fit_line)
    return prep_dist
コード例 #3
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ファイル: ksegment.py プロジェクト: vkhakham/k-segment
def calc_weighted_prep_dist(pw):
    prep_dist = np.full((len(pw), len(pw)), float("inf"))
    for index, value in np.ndenumerate(prep_dist):
        if index[0] < index[1]:
            if index[1] - index[0] == 1:
                prep_dist[index] = 0
                continue
            segment = pw[index[0] : index[1] + 1, :3]
            weights = pw[index[0] : index[1] + 1, 3:].flatten()
            best_fit_line = utils.calc_best_fit_line_polyfit(segment, weights)
            prep_dist[index] = utils.sqrd_dist_sum_weighted(segment, best_fit_line, w=weights)
    return prep_dist
コード例 #4
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ファイル: ksegment.py プロジェクト: vkhakham/k-segment
def calc_coreset_prep_dist(D):
    prep_dist = np.full((len(D), len(D)), float("inf"))
    for (first_coreset, second_coreset), value in np.ndenumerate(prep_dist):
        # we only want to calculate for segments that start in
        # starting coreset endpoints and end in ending coreset endpoints
        if first_coreset <= second_coreset:
            C = []
            for coreset in D[first_coreset:second_coreset+1]:
                C.append(coreset)
            coreset_of_coresets = Coreset.OneSegmentCorset(C, True)
            best_fit_line = utils.calc_best_fit_line_polyfit(coreset_of_coresets.repPoints, True)
            fitting_cost = utils.sqrd_dist_sum(coreset_of_coresets.repPoints, best_fit_line)*coreset_of_coresets.weight
            prep_dist[first_coreset, second_coreset] = fitting_cost
    return prep_dist
コード例 #5
0
ファイル: ksegment.py プロジェクト: vkhakham/k-segment
def calc_coreset_prep_dist(D):
    prep_dist = np.full((len(D), len(D)), float("inf"))
    for (first_coreset, second_coreset), value in np.ndenumerate(prep_dist):
        # we only want to calculate for segments that start in
        # starting coreset endpoints and end in ending coreset endpoints
        if first_coreset <= second_coreset:
            C = []
            W = []
            for coreset in D[first_coreset:second_coreset+1]:
                # segment = np.vstack([segment, coreset.C.repPoints]) if segment.size else coreset.C.repPoints
                C.append(coreset)
                W.append(coreset.C.weight)
            coreset_of_coresets = Coreset.OneSegmentCorset(C, True)
            best_fit_line = utils.calc_best_fit_line_polyfit(coreset_of_coresets.repPoints, True)
            # best_fit_line = utils.calc_best_fit_line(segment)
            # fitting_cost = 0
            # for i in xrange(len(C)):
            #    fitting_cost += utils.sqrd_dist_sum(C[i], best_fit_line)*W[i]
            fitting_cost = utils.sqrd_dist_sum(coreset_of_coresets.repPoints, best_fit_line)*coreset_of_coresets.weight
            prep_dist[first_coreset, second_coreset] = fitting_cost
    return prep_dist
コード例 #6
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ファイル: Coreset.py プロジェクト: vkhakham/k-segment
def BalancedPartition(P, a, bicritiriaEst, is_coreset=False):
    Q = []
    D = []
    points = P
    # add arbitrary item to list
    dimensions = points[0].C.repPoints.shape[1] if is_coreset else points.shape[1]
    if is_coreset:
        points.append(P[0])  # arbitrary coreset n+1
    else:
        points = np.vstack((points, np.zeros(dimensions)))  # arbitrary point n+1
    n = len(points)
    for i in xrange(0, n):
        Q.append(points[i])
        cost = one_seg_cost(np.asarray(Q), is_coreset)
        # print "bp cost:", cost, "points", Q
        # if current number of points can be turned into a coreset - 3 conditions :
        # 1) cost passed threshold
        # 2) number of points to be packaged greater than dimensions + 1
        # 3) number of points left greater then dimensions + 1 (so they could be packaged later)
        if cost > bicritiriaEst and (is_coreset or (len(Q) > dimensions + 1 and dimensions + 1 <= n - 1 - i)) or i == n - 1:
            if is_coreset and len(Q) == 1:
                if i != n - 1:
                    D.append(Q[0])
                    Q = []
                continue
            T = Q[:-1]
            C = OneSegmentCorset(T, is_coreset)
            g = utils.calc_best_fit_line_polyfit(OneSegmentCorset(np.asarray(T), is_coreset).repPoints)
            if is_coreset:
                b = T[0].b
                e = T[-1].e
            else:
                b = T[0][0]  # signal index of first item in T
                e = T[-1][0]  # signal index of last item in T
            D.append(coreset(C, g, b, e))
            Q = [Q[-1]]
    return D