def main(trainset, window, save_todir):
XPL = save_todir+'file.xpl'
#building xpl file
ensemble.build_xpl(trainset,window,XPL)
#reading the xpl file
xpl_data = xplutil.read_xpl(XPL)
decision = cl.make_decision(xpl_data.freq0,xpl_data.freq1)
size = xpl_data.winshape[0]*xpl_data.winshape[1]
xplutil.write_minterm_file(save_todir+'filename.mtm', np.array([range(size)]), xpl_data.winshape, xpl_data.data, decision)
def main(trainset, window, save_todir):
XPL = window+'.xpl'
#building xpl file
ensemble.build_xpl(trainset,window,XPL)
#reading xpl file
xpl_data = xplutil.read_xpl(XPL)
indices = np.array([0,1,3,4,5,7,8])
w0 = xpl_data.freq0.copy()
w1 = xpl_data.freq1.copy()
w0, w1 = cl.normalize_table(w0, w1)
hash, unique_array = project(xpl_data.data, indices)
sum0 = []
sum1 = []
for row in unique_array:
arr = hash.get(tuple(row.reshape(1,-1)[0]))
indexes = tuple(arr[0].reshape(1,-1)[0])
sum0.append(w0[[np.array(indexes)]].sum())
sum1.append(w1[[np.array(indexes)]].sum())
decision = cl.make_decision(sum0, sum1)
xplutil.write_minterm_file(save_todir+"mtmFile.mtm",indices, xpl_data.winshape, unique_array,decision)
def update_weights_setup():
w0 = np.random.uniform(size=100000)
w1 = np.random.uniform(size=100000)
dec = cl.make_decision(w0, w1)
return w0, w1, dec
