def main_all(size, nbiter, lr):
#neurons_init = None #ALLGISTS[0:size**2].reshape((size,size,-1))
neurons_init = ALLGISTS[0:size**2].reshape((size, size, -1))
k = km(size, DIM, neurons_init)
k.learn(ALLGISTS, nbiter, lr)
logging.info('Map computing ... finish')
save_img(k, 100000, thumb_size=64)
def main_all(size,nbiter,lr):
#neurons_init = None #ALLGISTS[0:size**2].reshape((size,size,-1))
neurons_init = ALLGISTS[0:size**2].reshape((size,size,-1))
k = km(size,DIM,neurons_init)
k.learn(ALLGISTS,nbiter,lr)
logging.info('Map computing ... finish')
save_img(k,100000,thumb_size=64)
def main(size, nbiter, lr):
NBCHANNEL = 3
init = np.random.random_integers(0, 255, size**2 * NBCHANNEL)
init = init.reshape(size, size, NBCHANNEL)
sample = np.array([[0, 0, 0], [255, 0, 0], [0, 255, 0], [0, 0, 255],
[255, 255, 0], [0, 255, 255]])
k = km(size, NBCHANNEL, init)
kohonen.save_rgb(k, 0)
k.learn(sample, nbiter, lr, callback=kohonen.save_rgb)
kohonen.save_rgb(k, 100000)
def main(size, nbiter, lr):
neurons_init = ALLGISTS[0:size**2].reshape((size, size, -1))
k = km(size, DIM, neurons_init)
pics = [
'forest_text111.jpg', 'mountain_n44001.jpg', 'coast_bea1.jpg',
'street_art1041.jpg', 'highway_bost161.jpg', 'opencountry_land381.jpg',
'insidecity_bost141.jpg', 'tallbuilding_a632011.jpg'
]
sample = np.array([GISTS[os.path.join(PICS_DIR, p)] for p in pics])
k.learn(sample, nbiter, lr) #,callback=save_img)
def main(size,nbiter,lr):
NBCHANNEL = 3
init = np.random.random_integers(0,255,size**2*NBCHANNEL)
init = init.reshape(size,size,NBCHANNEL)
sample = np.array([[0,0,0],
[255,0,0],
[0,255,0],
[0,0,255],
[255,255,0],
[0,255,255]])
k = km(size,NBCHANNEL,init)
kohonen.save_rgb(k,0)
k.learn(sample,nbiter,lr,callback=kohonen.save_rgb)
kohonen.save_rgb(k,100000)
def main(size,nbiter,lr):
neurons_init = ALLGISTS[0:size**2].reshape((size,size,-1))
k = km(size,DIM,neurons_init)
pics = ['forest_text111.jpg',
'mountain_n44001.jpg',
'coast_bea1.jpg',
'street_art1041.jpg',
'highway_bost161.jpg',
'opencountry_land381.jpg',
'insidecity_bost141.jpg',
'tallbuilding_a632011.jpg']
sample = np.array([ GISTS[os.path.join(PICS_DIR,p)] for p in pics ])
k.learn(sample,nbiter,lr)#,callback=save_img)