compmonks

newmtl polkadots
Ns 10.0000
Ni 1.5000
d 1.0000
Tr 0.0000
Tf 1.0000 1.0000 1.0000
illum 2
Ka 0 0 0 
Kd 0 0 0 
Ks 0.0000 0.0000 0.0000

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def onlinePredictBy(self, newdata, k=5, wt='distance'):
	"""Return online predicted values of a target array"""
	self.newdata = newdata
	# build an index array except the ones containing the target
	self.dim = newdata.shape[1]
	self.ind = np.arange(0, self.dim)
	self.ind = np.setdiff1d(self.ind,self.target)
	self.x = self.SOM.codebook.matrix[:,self.ind]
	self.y = self.SOM.codebook.matrix[:,self.target]
	self.n_neighbors = k

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#ie: allData.shape = (587, 165)
#    old_codebook_matrix.shape = (2500, 165)
from sklearn.metrics import accuracy_score
#...

if the_codebook != None:
    the_codebook = h5py.File(str(the_codebook),'r')
    old_codebook_matrix = the_codebook['codebook/matrix']
    split_point = math.floor(allData.shape[0]/2)
    train_set = allData[:split_point] # splitting allData for train/test

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class SOM(object):
	def __init__(self,
			data,
			neighborhood,
			prev_codebook_matrix=None, # initialization with previous codebook matrix
			normalizer=None,
			mapsize=None,
			mask=None,
			mapshape='planar',
			lattice='rect',

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# ex: data.spahe -> (603, 165)
# bmus shape - > (603,)


def plotOnSignal(self,s=10,cmap="viridis",data):
  """Plot Bmus on top of the rawData. Plotting in Context."""
  bmus = self.SOM.project_data(data)
  rows = data.shape[0]
  #
  plt.title('BMUS on FEEDBACK DATA')

compmonks

// directory
//
// public(folder)
// --app.js
// --js(folder)
// ----form.js


var contactForm = document.getElementById( 'contactForm' );
			new stepsForm( contactForm, {

compmonks

// directory
//
// public(folder)
// --app.js
// --js(folder)
// ----form.js



// app.js