# The quantization error: average distance between each data vector and its BMU.
# The topographic error: the proportion of all data vectors for which first and second BMUs are not adjacent units.
topographic_error = sm.calculate_topographic_error()
quantization_error = np.mean(sm._bmu[1])
print("Topographic error = %s; Quantization error = %s" %
(topographic_error, quantization_error))
# component planes view
from sompy.visualization.mapview import View2D
view2D = View2D(10, 10, "rand data", text_size=12)
view2D.show(sm, col_sz=4, which_dim="all", desnormalize=True)
# U-matrix plot
from sompy.visualization.umatrix import UMatrixView
umat = UMatrixView(width=10, height=10, title='U-matrix')
umat.show(sm)
# do the K-means clustering on the SOM grid, sweep across k = 2 to 20
from sompy.visualization.hitmap import HitMapView
K = 20 # stop at this k for SSE sweep
K_opt = 18 # optimal K already found
[labels, km, norm_data] = sm.cluster(K, K_opt)
hits = HitMapView(20, 20, "Clustering", text_size=12)
a = hits.show(sm)
import gmplot
gmap = gmplot.GoogleMapPlotter(54.2, -124.875224, 6)
j = 0
for i in km.cluster_centers_:
# The quantization error: average distance between each data vector and its BMU.
# The topographic error: the proportion of all data vectors for which first and second BMUs are not adjacent units.
topographic_error = som.calculate_topographic_error()
quantization_error = np.mean(som._bmu[1])
print "Topographic error = %s; Quantization error = %s" % (topographic_error,
quantization_error)
from sompy.visualization.mapview import View2D
view2D = View2D(4, 4, "rand data", text_size=16)
view2D.show(som, col_sz=2, which_dim="all", denormalize=True)
# U-matrix plot
from sompy.visualization.umatrix import UMatrixView
umat = UMatrixView(width=20, height=20, title='U-matrix')
umat.show(som)
from sompy.visualization.hitmap import HitMapView
from sompy.visualization.bmuhits import BmuHitsView
bmuhitsview = BmuHitsView(12, 12, 'Data per node', text_size=24)
bmuhitsview.show(som,
anotate=False,
onlyzeros=False,
labelsize=7,
logaritmic=False)
Kluster = som.cluster(5)
hits = HitMapView(20, 20, "K-Means Clustering", text_size=16)
a = hits.show(som, anotate=False, labelsize=7, cmap='viridis')
n_job=1, verbose=None, train_rough_len=2,
train_finetune_len=100) # I left some of the codes as the example provided
# plot the results, components map
from sompy.visualization.mapview import View2D
view2D = View2D(20, 20, "", text_size=12)
view2D.show(sm, col_sz=3, which_dim="all", denormalize=False)
# Hit maps
from sompy.visualization.bmuhits import BmuHitsView
vhts = BmuHitsView(15, 10, "Hits Map", text_size=12)
vhts.show(sm,
anotate=False,
onlyzeros=False,
labelsize=12,
cmap="jet",
logaritmic=False)
# U martix
from sompy.visualization.umatrix import UMatrixView
u = UMatrixView(15,
15,
'umatrix',
show_axis=True,
text_size=12,
show_text=False)
UMAT = u.build_u_matrix(sm, distance=1, row_normalized=False)
u.show(sm)
def som_u_matrix(som):
# U-matrix plot
umat = UMatrixView(width=10, height=10, title='U-matrix')
umat.show(som)