def GPDM_solver(self):
train_data = []
print(self.gene_data)
for key_gene in self.select_gene:
train_data.append(self.gene_data[key_gene])
self.train_data = np.array(train_data).T
output = GPLVM(self.train_data, self.latent_dim, init='PCA')
output.optimize(messages=True, max_iters=20)
return output
plt.tick_params(bottom=False,
left=False,
right=False,
top=False)
plt.gca().spines['right'].set_visible(False)
plt.gca().spines['top'].set_visible(False)
plt.gca().spines['left'].set_visible(False)
plt.gca().spines['bottom'].set_visible(False)
plt.savefig(f_name)
if __name__ == "__main__":
np.random.seed(3)
image = np.asarray(Image.open('./data/shobon.png').convert('L'))
# binarization
a = np.where(image < 240)
shobon = np.c_[a[1], np.flipud(a[0])]
img_save(shobon, f_name='shobon.png')
# high dimension
dim = 100
w = np.random.normal(size=2*dim).reshape(2, dim)
high = shobon @ w
high = high + np.random.normal(size=high.shape[0]*high.shape[1]).reshape(high.shape[0], high.shape[1])
# latent
gplvm = GPLVM(high, 2)
latent = gplvm.X
img_save(latent, f_name='latent.png')
