예제 #1
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 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
예제 #2
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 def plot_latent(self, *args, **kwargs):
     input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
     pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')
예제 #3
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 def plot(self):
     GPLVM.plot(self)
     # passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
     mu, var, upper, lower = SparseGPRegression.predict(self, self.Z + np.random.randn(*self.Z.shape) * 0.0001)
     pb.plot(mu[:, 0] , mu[:, 1], 'ko')
예제 #4
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파일: sparse_gplvm.py 프로젝트: jaidevd/GPy
 def plot_latent(self, *args, **kwargs):
     input_1, input_2 = GPLVM.plot_latent(*args, **kwargs)
     pb.plot(m.Z[:, input_1], m.Z[:, input_2], '^w')
예제 #5
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파일: sparse_gplvm.py 프로젝트: jaidevd/GPy
 def plot(self):
     GPLVM.plot(self)
     # passing Z without a small amout of jitter will induce the white kernel where we don;t want it!
     mu, var, upper, lower = SparseGPRegression.predict(self, self.Z + np.random.randn(*self.Z.shape) * 0.0001)
     pb.plot(mu[:, 0] , mu[:, 1], 'ko')
예제 #6
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    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')