使用带有pdist和squareform的nparray创建距离矩阵

Question

我正在尝试使用DBSCAN(scikit learn implementation)和位置数据进行集群.我的数据是np数组格式,但要使用带有Haversine公式的DBSCAN,我需要创建一个距离矩阵.当我尝试这样做时,我得到以下错误('模块'不可调用错误.)从我在线阅读这是一个导入错误,但我很确定不是我的情况.我已经创建了自己的半身距离公式,但我确定错误不是这个.

这是我的输入数据,一个np数组(ResultArray).

[[ 53.3252628   -6.2644198 ]
[ 53.3287395   -6.2646543 ]
[ 53.33321202  -6.24785807]
[ 53.3261015   -6.2598324 ]
[ 53.325291    -6.2644105 ]
[ 53.3281323   -6.2661467 ]
[ 53.3253074   -6.2644483 ]
[ 53.3388147   -6.2338417 ]
[ 53.3381102   -6.2343826 ]
[ 53.3253074   -6.2644483 ]
[ 53.3228188   -6.2625379 ]
[ 53.3253074   -6.2644483 ]]

这是错误的代码行.

distance_matrix = sp.spatial.distance.squareform(sp.spatial.distance.pdist
(ResultArray,(lambda u,v: haversine(u,v))))

这是错误消息:

File "Location.py", line 48, in <module>
distance_matrix = sp.spatial.distance.squareform(sp.spatial.distance.pdist
(ResArray,(lambda u,v: haversine(u,v))))
File "/usr/lib/python2.7/dist-packages/scipy/spatial/distance.py", line 1118, in pdist
dm[k] = dfun(X[i], X[j])
File "Location.py", line 48, in <lambda>
distance_matrix = sp.spatial.distance.squareform(sp.spatial.distance.pdist
(ResArray,(lambda u,v: haversine(u,v))))
TypeError: 'module' object is not callable

我将scipy导入为sp.(导入scipy为sp)

Answer 1

使用Scipy,您可以根据此链接中的文档建议定义自定义距离函数,并在此处进行报告以方便起见:

Y = pdist(X, f)

Computes the distance between all pairs of vectors in X using the user supplied 2-arity function f. For example, Euclidean distance between the vectors could be computed as follows:

dm = pdist(X, lambda u, v: np.sqrt(((u-v)**2).sum()))

在这里,我通过此链接报告了我的代码版本:

from numpy import sin,cos,arctan2,sqrt,pi # import from numpy
# earth's mean radius = 6,371km
EARTHRADIUS = 6371.0

def getDistanceByHaversine(loc1, loc2):
    '''Haversine formula - give coordinates as a 2D numpy array of
    (lat_denter link description hereecimal,lon_decimal) pairs'''
    #      
    # "unpack" our numpy array, this extracts column wise arrays
    lat1 = loc1[1]
    lon1 = loc1[0]
    lat2 = loc2[1]
    lon2 = loc2[0]
    #
    # convert to radians ##### Completely identical
    lon1 = lon1 * pi / 180.0
    lon2 = lon2 * pi / 180.0
    lat1 = lat1 * pi / 180.0
    lat2 = lat2 * pi / 180.0
    #
    # haversine formula #### Same, but atan2 named arctan2 in numpy
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = (sin(dlat/2))**2 + cos(lat1) * cos(lat2) * (sin(dlon/2.0))**2
    c = 2.0 * arctan2(sqrt(a), sqrt(1.0-a))
    km = EARTHRADIUS * c
    return km

并按以下方式致电:

D = spatial.distance.pdist(A, lambda u, v: getDistanceByHaversine(u,v))

在我的实现中,矩阵A的第一列是经度值,第二列是以十进制度表示的纬度值.

Answer 2

请参考@TommasoF 的回答。这个答案是错误的：pdist允许选择自定义距离函数。一旦不再被选为正确答案，我将删除答案。

Simplyscipy不允许pdist传入自定义距离函数。正如您在文档中所读到的，您有一些选择，但哈弗侧距离不在支持的指标列表中。

（Matlabpdist确实支持该选项，请参见此处）

你需要“手动”进行计算，即使用循环，这样的事情会起作用：

from numpy import array,zeros

def haversine(lon1, lat1, lon2, lat2):
    """  See the link below for a possible implementation """
    pass

#example input (your's, truncated)
ResultArray = array([[ 53.3252628, -6.2644198 ],
                     [ 53.3287395  , -6.2646543 ],
                     [ 53.33321202 , -6.24785807],
                     [ 53.3253074  , -6.2644483 ]])

N = ResultArray.shape[0]
distance_matrix = zeros((N, N))
for i in xrange(N):
    for j in xrange(N):
        lati, loni = ResultArray[i]
        latj, lonj = ResultArray[j]
        distance_matrix[i, j] = haversine(loni, lati, lonj, latj)
        distance_matrix[j, i] = distance_matrix[i, j]

print distance_matrix
[[ 0.          0.38666203  1.41010971  0.00530489]
 [ 0.38666203  0.          1.22043364  0.38163748]
 [ 1.41010971  1.22043364  0.          1.40848782]
 [ 0.00530489  0.38163748  1.40848782  0.        ]]

仅供参考，可以在此处找到 Haverside 的 Python 实现。