pysal.lib.weights.
KNN
(data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean', **kwargs)[source]¶Creates nearest neighbor weights matrix based on k nearest neighbors.
Parameters: 


Returns: 

See also
pysal.lib.weights.weights.W
Notes
Ties between neighbors of equal distance are arbitrarily broken.
Examples
>>> import pysal.lib
>>> points = [(10, 10), (20, 10), (40, 10), (15, 20), (30, 20), (30, 30)]
>>> kd = pysal.lib.cg.kdtree.KDTree(np.array(points))
>>> wnn2 = pysal.lib.weights.KNN(kd, 2)
>>> [1,3] == wnn2.neighbors[0]
True
ids
>>> wnn2 = KNN(kd,2)
>>> wnn2[0]
{1: 1.0, 3: 1.0}
>>> wnn2[1]
{0: 1.0, 3: 1.0}
now with 1 rather than 0 offset
>>> wnn2 = pysal.lib.weights.KNN(kd, 2, ids=range(1,7))
>>> wnn2[1]
{2: 1.0, 4: 1.0}
>>> wnn2[2]
{1: 1.0, 4: 1.0}
>>> 0 in wnn2.neighbors
False
Attributes: 


Methods
asymmetry ([intrinsic]) 
Asymmetry check. 
from_adjlist (adjlist[, focal_col, …]) 
Return an adjacency list representation of a weights object. 
from_array (array, *args, **kwargs) 
Creates nearest neighbor weights matrix based on k nearest neighbors. 
from_dataframe (df[, geom_col, ids]) 
Make KNN weights from a dataframe. 
from_networkx (graph[, weight_col]) 
Convert a networkx graph to a PySAL W object. 
from_shapefile (filepath, *args, **kwargs) 
Nearest neighbor weights from a shapefile. 
full () 
Generate a full numpy array. 
get_transform () 
Getter for transform property. 
plot (gdf[, indexed_on, ax, color, node_kws, …]) 
Plot spatial weights objects. 
remap_ids (new_ids) 
In place modification throughout W of id values from w.id_order to new_ids in all 
reweight ([k, p, new_data, new_ids, inplace]) 
Redo KNearest Neighbor weights construction using given parameters 
set_shapefile (shapefile[, idVariable, full]) 
Adding meta data for writing headers of gal and gwt files. 
set_transform ([value]) 
Transformations of weights. 
symmetrize ([inplace]) 
Construct a symmetric KNN weight. 
to_WSP () 
Generate a WSP object. 
to_adjlist ([remove_symmetric, focal_col, …]) 
Compute an adjacency list representation of a weights object. 
to_networkx () 
Convert a weights object to a networkx graph 
from_WSP  
from_file 
__init__
(data, k=2, p=2, ids=None, radius=None, distance_metric='euclidean', **kwargs)[source]¶Initialize self. See help(type(self)) for accurate signature.
Methods
__init__ (data[, k, p, ids, radius, …]) 
Initialize self. 
asymmetry ([intrinsic]) 
Asymmetry check. 
from_WSP (WSP[, silence_warnings]) 

from_adjlist (adjlist[, focal_col, …]) 
Return an adjacency list representation of a weights object. 
from_array (array, *args, **kwargs) 
Creates nearest neighbor weights matrix based on k nearest neighbors. 
from_dataframe (df[, geom_col, ids]) 
Make KNN weights from a dataframe. 
from_file ([path, format]) 

from_networkx (graph[, weight_col]) 
Convert a networkx graph to a PySAL W object. 
from_shapefile (filepath, *args, **kwargs) 
Nearest neighbor weights from a shapefile. 
full () 
Generate a full numpy array. 
get_transform () 
Getter for transform property. 
plot (gdf[, indexed_on, ax, color, node_kws, …]) 
Plot spatial weights objects. 
remap_ids (new_ids) 
In place modification throughout W of id values from w.id_order to new_ids in all 
reweight ([k, p, new_data, new_ids, inplace]) 
Redo KNearest Neighbor weights construction using given parameters 
set_shapefile (shapefile[, idVariable, full]) 
Adding meta data for writing headers of gal and gwt files. 
set_transform ([value]) 
Transformations of weights. 
symmetrize ([inplace]) 
Construct a symmetric KNN weight. 
to_WSP () 
Generate a WSP object. 
to_adjlist ([remove_symmetric, focal_col, …]) 
Compute an adjacency list representation of a weights object. 
to_networkx () 
Convert a weights object to a networkx graph 
Attributes
asymmetries 
List of id pairs with asymmetric weights. 
cardinalities 
Number of neighbors for each observation. 
component_labels 
Store the graph component in which each observation falls. 
diagW2 
Diagonal of \(WW\). 
diagWtW 
Diagonal of \(W^{'}W\). 
diagWtW_WW 
Diagonal of \(W^{'}W + WW\). 
histogram 
Cardinality histogram as a dictionary where key is the id and value is the number of neighbors for that unit. 
id2i 
Dictionary where the key is an ID and the value is that ID’s index in W.id_order. 
id_order 
Returns the ids for the observations in the order in which they would be encountered if iterating over the weights. 
id_order_set 
Returns True if user has set id_order, False if not. 
islands 
List of ids without any neighbors. 
max_neighbors 
Largest number of neighbors. 
mean_neighbors 
Average number of neighbors. 
min_neighbors 
Minimum number of neighbors. 
n 
Number of units. 
n_components 
Store whether the adjacency matrix is fully connected. 
neighbor_offsets 
Given the current id_order, neighbor_offsets[id] is the offsets of the id’s neighbors in id_order. 
nonzero 
Number of nonzero weights. 
pct_nonzero 
Percentage of nonzero weights. 
s0 
s0 is defined as 
s1 
s1 is defined as 
s2 
s2 is defined as 
s2array 
Individual elements comprising s2. 
sd 
Standard deviation of number of neighbors. 
sparse 
Sparse matrix object. 
transform 
Getter for transform property. 
trcW2 
Trace of \(WW\). 
trcWtW 
Trace of \(W^{'}W\). 
trcWtW_WW 
Trace of \(W^{'}W + WW\). 