sys.path += ['..']
import numpy as np
import scipy.stats
from graph_labeling import graph_cut, make_neighbourhood
from scipy.spatial import cKDTree
from case_study_bm import setup_case_study_ore
CHECK_VALID = False
if __name__ == "__main__":
locations,data,min_values,max_values,scale,var_types,targets = setup_case_study_ore()
seed = 1634120
np.random.seed(seed)
lambda_value = 0.25
NC = 3
target = False
force = False
file_template = '../results/bm_{set}_swfc_%d.csv'%NC
best_centroids = np.loadtxt(file_template.format(set='centroids'),delimiter=",")
best_weights = np.loadtxt(file_template.format(set='weights'),delimiter=",")
best_u = np.loadtxt(file_template.format(set='u'),delimiter=",")
clusters = np.argmax(best_u,axis=1)
import clusteringlib as cl
import numpy as np
import scipy.stats
import clustering_ga
from scipy.spatial.distance import pdist
from sklearn.cluster import KMeans
from cluster_utils import fix_weights
CHECK_VALID = False
from case_study_bm import attributes, setup_case_study_ore, setup_case_study_all, setup_distances
if __name__ == "__main__":
locations, data, min_values, max_values, scale, var_types, categories = setup_case_study_ore(
a=0.999)
N, ND = data.shape
print(N, ND)
#print(min_values)
#print(max_values)
#print(scale)
seed = 1634120
#targets = np.asfortranarray(np.percentile(data[:,-1], [15,50,85]),dtype=np.float32)
#var_types[-1] = 2
#print('targets',targets)
m = 2.0
