Beispiel #1
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    def test_distance(self):
        point1 = (20, 30, 40)
        point2 = (20, 30, 40)
        point3 = (250, 240, 230)

        print "Running test 1.a"
        assert kmeans.distance(point1, point2) == 0
        print "Running test 1.b"
        assert kmeans.distance(point1, point3) == 133100
Beispiel #2
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def nearest(point, cluster_centers):
    min_dist = FLOAT_MAX
    m = np.shape(cluster_centers)[0]  # 当前已经初始化的聚类中心的个数
    for i in range(m):
        # 计算point与每个聚类中心之间的距离
        d = distance(point, cluster_centers[
            i, ])  #计算当前点与已经初始化的每个点的距离,并且得到最小值,即假设该点与初始化的第二个簇中心最短
        # 选择最短距离
        if min_dist > d:
            min_dist = d
    return min_dist
Beispiel #3
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def nearest(point, cluster_centers):
    '''
    计算point和cluster_centers之间的最小距离
    input:  point(mat):当前的样本点
            cluster_centers(mat):当前已经初始化的聚类中心
    output: min_dist(float):点point和当前的聚类中心之间的最短距离
    '''
    min_dist = FLOAT_MAX
    m = np.shape(cluster_centers)[0]  # 当前已经初始化的聚类中心的个数
    for i in range(m):
        # 计算point与每个聚类中心之间的距离
        d = distance(point, cluster_centers[i, ])
        # 选择最短距离
        if min_dist > d:
            min_dist = d
    return min_dist
def generate_path(probability_matrix, state_prototypes, action_prototypes, state_indices, iterations):
    state_index = random.sample(state_indices, 1)[0]  # select random point
    path = []
    current_position = state_prototypes[state_index]  # choose closest state prototype
    for i in range(iterations):
        path.append(current_position)
        # following should not be the case but who knows what is going on with 2nd map
        if np.sum(probability_matrix[state_index]) == 0:
            best_action_index = np.argmax((probability_matrix[state_index]))
        else:
            # choose best action using matrix of joint probabilities
            best_action_index = np.argmax((probability_matrix[state_index] / np.sum(probability_matrix[state_index])))
        best_action = action_prototypes[best_action_index]
        current_position = current_position + best_action
        dist = [kmeans.distance(current_position, k) for k in state_prototypes]
        min_index, = np.where(dist == np.min(dist))  # choose closest prototype
        state_index = min_index[0]
    return path
Beispiel #5
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def problem2_4_4():
    data, featureNames = load_data()
    k = 6
    iniCenters = orderedCenters(data, k)
    clusters, centers = kmeans(data, k,
                               initialCenters=iniCenters)
    avgAll = avg(data.keys(), data)

    print "average: %s" % avgAll
    print "########################"
    print "cluster # -> (distance from average)"
    print "    [# in cluster]:[items in cluster]"
    print ""
    
    for c in clusters:
        print "cluster %d -> %s: " % (c, distance(avgAll, centers[c]))
        print "    [%d]:%s " % (len(clusters[c]), clusters[c])
        print ""
Beispiel #6
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def generate_path(probability_matrix, state_prototypes, action_prototypes,
                  state_indices, iterations):
    state_index = random.sample(state_indices, 1)[0]  # select random point
    path = []
    current_position = state_prototypes[
        state_index]  # choose closest state prototype
    for i in range(iterations):
        path.append(current_position)
        # following should not be the case but who knows what is going on with 2nd map
        if np.sum(probability_matrix[state_index]) == 0:
            best_action_index = np.argmax((probability_matrix[state_index]))
        else:
            # choose best action using matrix of joint probabilities
            best_action_index = np.argmax(
                (probability_matrix[state_index] /
                 np.sum(probability_matrix[state_index])))
        best_action = action_prototypes[best_action_index]
        current_position = current_position + best_action
        dist = [kmeans.distance(current_position, k) for k in state_prototypes]
        min_index, = np.where(dist == np.min(dist))  # choose closest prototype
        state_index = min_index[0]
    return path
Beispiel #7
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 def test_distance(self, two_dimensional_points, three_dimensional_points):
     assert distance(*two_dimensional_points) > 7.06
     assert distance(*two_dimensional_points) < 7.08
Beispiel #8
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 def test_distance(self, two_dimensional_points, three_dimensional_points):
     assert distance(*two_dimensional_points) > 7.06
     assert distance(*two_dimensional_points) < 7.08
Beispiel #9
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 def test_distance(self):
     sample1 = [0.074936026, 0.0683657238]
     center1 = [0.079113924099999997, 0.065822784800000006]
     dist = distance(sample1, center1)
     self.assertAlmostEqual(dist, 0.00489095)
Beispiel #10
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def rbfMult(x, c, s):
    return (1 + (km.distance(x, c))**2)**0.5
Beispiel #11
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def rbfGaus(x, c, s):
    return np.exp(-1 / (2 * s**2) * (km.distance(x, c))**2)
Beispiel #12
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def rbfMultIn(x, c, s):
    return 1 / (1 + (km.distance(x, c))**2)**0.5