示例#1
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model = BSC_ET(D, H, Hprime, gamma, to_learn=['W','sigma','pi','mu'])

data = {'y':patches}


out_fname = output_path + "/data.h5"


#setting up logging/output
print_list = ('T', 'Q', 'pi', 'sigma', 'N', 'MAE', 'L')
dlog.set_handler(print_list, TextPrinter)
h5store_list = ('W', 'pi', 'sigma', 'y', 'MAE', 'N','L','Q','mu')
dlog.set_handler(h5store_list, StoreToH5, output_path +'/result.h5')

###### Initialize model #######
# Initialize (Random) model parameters
model_params = model.standard_init(data)

#model_params['mu'] = np.mean(data['y'],axis=0)

# Create and start EM annealing
em = EM(model=model, anneal=anneal)
em.data = data
em.lparams = model_params
em.run()

dlog.close(True)
pprint("Done")


示例#2
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# Parameters
rf_shape = (26, 26)
H = 16

# Configure Data-Logger
dlog.start_gui(GUI)
dlog.set_handler('W', RFViewer, rf_shape=rf_shape)
dlog.set_handler('S', YTPlotter)
dlog.set_handler('C', YTPlotter)
dlog.set_handler(('T', 'S', 'C'), TextPrinter)

# And GO!
D = rf_shape[0] * rf_shape[1]

Wshape = (H, D)
i = 0
for T in np.linspace(0., 20, 50):
    i = i + 1
    pprint("%i th iteration..." % i)

    W = np.random.normal(size=Wshape)
    dlog.append_all({
        'T': T,
        'W': W,
        'S': np.sin(T),
        'C': np.cos(T),
    })

dlog.close()
示例#3
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    W_gt = W_gt.reshape((H, D))

    # Prepare model...
    model = MCA_ET(D, H, Hprime, gamma)
    gt_params = {"W": W_gt, "pi": 2.0 / H, "sigma": 1.00}

    # Generate trainig data
    my_N = N // comm.size
    my_data = model.generate_data(gt_params, my_N)
    dlog.append("y", my_data["y"][0:25, :])

    # Initialize model parameters (to be learned)
    params = {
        #    'W'     : W_gt,
        "W": np.abs(5 + np.random.normal(size=W_gt.shape)),
        "pi": 2 / H,
        "sigma": 5.00,
    }
    # params = model.noisify_params(params, anneal)
    params = comm.bcast(params)

    # Create and start EM annealing
    em = EM(model=model, anneal=anneal)
    em.data = my_data
    em.lparams = params
    em.run()

    dlog.close()

    # print(em.lparams['W'])
示例#4
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    if W_learn == 'mean':
        lparams = {
            'W': mean_W,
            'pi': pi_learn,
            'sigma': sigma_learn,
            'mu': mu_learn
        }
    elif W_learn == 'ground_truth':
        lparams = {'W': W_gt, 'pi': pi_gt, 'sigma': sigma_gt, 'mu': mu_gt}
    else:
        lparams = {
            'W': W_learn,
            'pi': pi_learn,
            'sigma': sigma_learn,
            'mu': mu_learn
        }
    lparams = model.noisify_params(lparams, anneal)

    # Create and start EM annealing
    em = EM(model=model, anneal=anneal)
    em.data = my_data
    em.lparams = lparams
    em.run()
    pprint("Done with ET, Calculating MAE...")
    MAE = check_basis(em.lparams['W'], W_gt)
    if MAE == None:
        MAE = np.inf
    dlog.append('MAE', MAE[0])
    dlog.close(True)
    pprint("Done")