def max_p(X, w, threshold_variable="count", threshold=10, **kwargs):
"""Max-p clustering algorithm
:cite:`Duque2012`
Parameters
----------
X : array-like
n x k attribute data
w : PySAL W instance
spatial weights matrix
threshold_variable : str, default:"count"
attribute variable to use as floor when calculate
threshold : int, default:10
integer that defines the upper limit of a variable that can be grouped
into a single region
Returns
-------
model: region MaxPRegionsHeu instance
"""
model = MaxPRegionsHeu()
model.fit_from_w(w, X.values, threshold_variable, threshold)
return model
def test_w_multi_attr():
print(double_threshold)
cluster_object = MaxPRegionsHeu()
cluster_object.fit_from_w(w, double_attr, double_spatially_extensive_attr,
threshold=double_threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_w_multi_attr():
print(double_threshold)
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_w(w, double_attr, double_spatially_extensive_attr,
threshold=double_threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_w_basic():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_w(w,
attr,
spatially_extensive_attr,
threshold=threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)
def test_w_basic():
cluster_object = MaxPRegionsHeu(random_state=0)
cluster_object.fit_from_w(w, attr, spatially_extensive_attr,
threshold=threshold)
result = region_list_from_array(cluster_object.labels_)
compare_region_lists(result, optimal_clustering)