def one_axis_test():
data = pickle.load(open("../mouseretina/component.2.3.pickle", 'r'))
conn_true = data['conn_dist_hist']
conn_false = data['noconn_dist_hist']
print "true_count = ", len(conn_true)
print "false_count=", len(conn_false)
p_max = 0.98
p_min = 0.02
mu_hp = 2.0
lamb_hp = 5.0
mu = 10.0
lamb = 10.0
pylab.figure()
pylab.plot(data['bins'][:-1], data['p'])
for S in range(10):
mu, lamb = slice(conn_true,
conn_false,
mu,
lamb,
mu_hp,
lamb_hp,
p_min,
p_max,
ITERS=30)
pylab.plot(data['fine_bins'],
logistic(data['fine_bins'], mu, lamb) * (p_max - p_min) +
p_min,
c='r')
pylab.show()
def score_component(conn_true, conn_false, mu, lamb, mu_hp, lamb_hp,
p_min, p_max):
score = 0.0
p_range = p_max - p_min
for d in conn_true:
p = logistic(d, mu, lamb) * p_range + p_min
score += np.log(p)
for d in conn_true:
p = logistic(d, mu, lamb) * p_range + p_min
score += np.log(1.0 - p)
prior = log_exp_dist(mu, mu_hp)
prior += log_exp_dist(lamb, lamb_hp)
score += prior
return score
def score_component(conn_true, conn_false, mu, lamb, mu_hp, lamb_hp, p_min,
p_max):
score = 0.0
p_range = p_max - p_min
for d in conn_true:
p = logistic(d, mu, lamb) * p_range + p_min
score += np.log(p)
for d in conn_true:
p = logistic(d, mu, lamb) * p_range + p_min
score += np.log(1.0 - p)
prior = log_exp_dist(mu, mu_hp)
prior += log_exp_dist(lamb, lamb_hp)
score += prior
return score
def grid():
data = pickle.load(open("../mouseretina/component.0.1.pickle", 'r'))
conn_true = data['conn_dist_hist']
conn_false = data['noconn_dist_hist']
N = 200
p_max = 0.95
p_min = 0.05
x = np.zeros((N, N))
mus = np.linspace(0.0, 10, N)
lambs = np.linspace(0.0, 4, N)
for i, mu in enumerate(mus):
for j, lamb in enumerate(lambs):
mu_hp = 10.0
lamb_hp = 10.0
x[i, j] = score_component(conn_true, conn_false, mu, lamb, mu_hp,
lamb_hp, p_min, p_max)
x -= np.max(x)
mu_i, lamb_i = np.unravel_index(np.argmax(x), x.shape)
pylab.figure()
pylab.plot(data['bins'][:-1], data['p'])
pylab.plot(data['bins'],
logistic(data['bins'], mus[mu_i], lambs[lamb_i]) *
(p_max - p_min) + p_min,
c='r')
pylab.figure()
pylab.imshow(x)
pylab.colorbar()
pylab.figure()
for ki, k in enumerate([1, 10, 20, 30]):
pylab.subplot(4, 1, ki + 1)
p = np.exp(x[:, k]) / np.sum(np.exp(x[:, k]))
pylab.plot(mus, p)
pylab.grid()
pylab.show()
def grid():
data = pickle.load(open("../mouseretina/component.0.1.pickle", 'r'))
conn_true = data['conn_dist_hist']
conn_false = data['noconn_dist_hist']
N = 200
p_max = 0.95
p_min = 0.05
x = np.zeros((N, N))
mus = np.linspace(0.0, 10, N)
lambs = np.linspace(0.0, 4, N)
for i, mu in enumerate(mus):
for j, lamb in enumerate(lambs):
mu_hp = 10.0
lamb_hp = 10.0
x[i, j] = score_component(conn_true, conn_false, mu, lamb,
mu_hp, lamb_hp,
p_min, p_max)
x -= np.max(x)
mu_i, lamb_i = np.unravel_index(np.argmax(x), x.shape)
pylab.figure()
pylab.plot(data['bins'][:-1], data['p'])
pylab.plot(data['bins'], logistic(data['bins'], mus[mu_i],
lambs[lamb_i])*(p_max - p_min) + p_min, c='r')
pylab.figure()
pylab.imshow(x)
pylab.colorbar()
pylab.figure()
for ki, k in enumerate([1, 10, 20, 30]):
pylab.subplot(4, 1, ki+1)
p = np.exp(x[:, k]) / np.sum(np.exp(x[:, k]))
pylab.plot(mus, p)
pylab.grid()
pylab.show()
def one_axis_test():
data = pickle.load(open("../mouseretina/component.2.3.pickle", 'r'))
conn_true = data['conn_dist_hist']
conn_false = data['noconn_dist_hist']
print "true_count = ", len(conn_true)
print "false_count=", len(conn_false)
p_max = 0.98
p_min = 0.02
mu_hp = 2.0
lamb_hp = 5.0
mu = 10.0
lamb=10.0
pylab.figure()
pylab.plot(data['bins'][:-1], data['p'])
for S in range(10):
mu, lamb = slice(conn_true, conn_false, mu, lamb,
mu_hp, lamb_hp, p_min, p_max, ITERS=30)
pylab.plot(data['fine_bins'], logistic(data['fine_bins'], mu,
lamb)*(p_max - p_min) + p_min, c='r')
pylab.show()
