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| --------------------------------------------------------------------------- | |
| ValueError Traceback (most recent call last) | |
| <ipython-input-65-a4128a1c8e7f> in <module>() | |
| 14 gp = pm.gp.Marginal(cov_func=cov_func) | |
| 15 f = gp.marginal_likelihood('f',X,y,0.1) | |
| ---> 16 f_star = gp.conditional('fstar',Xnew) | |
| 17 | |
| ~/anaconda3/envs/kerastf/lib/python3.6/site-packages/pymc3-3.3rc2-py3.6.egg/pymc3/gp/gp.py in conditional(self, name, Xnew, pred_noise, given, **kwargs) | |
| 501 |
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| import numpy as np | |
| import tensorflow as tf | |
| from gpflow.likelihoods import Likelihood | |
| from gpflow import densities | |
| from gpflow.decors import params_as_tensors | |
| from gpflow.params import Parameter | |
| from gpflow.transforms import Transform, positive, Chain | |
| from gpflow import settings |
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| import gpflow as gp | |
| import numpy as np | |
| import pylab as plt | |
| import tensorflow as tf | |
| X = np.array([[ 1.16527441e+09], | |
| [ 1.16527442e+09], | |
| [ 1.16527443e+09], | |
| [ 1.16527443e+09], | |
| [ 1.16527444e+09], |
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| package com.tactico.tm.bugs; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.BackpropType; | |
| import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.Updater; | |
| import org.deeplearning4j.nn.conf.inputs.InputType; | |
| import org.deeplearning4j.nn.conf.layers.RnnOutputLayer; | |
| import org.deeplearning4j.nn.weights.WeightInit; |
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| eval cmake | |
| Running windows | |
| PACKAGING = none | |
| BUILD = release | |
| CHIP = cuda | |
| ARCH = x86-64 | |
| CHIP_VERSION = | |
| GPU_COMPUTE_CAPABILITY = 50 | |
| EXPERIMENTAL = no | |
| LIBRARY TYPE = dynamic |
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| eval cmake | |
| Running windows | |
| PACKAGING = none | |
| BUILD = release | |
| CHIP = cpu | |
| ARCH = x86-64 | |
| CHIP_VERSION = | |
| GPU_COMPUTE_CAPABILITY = all | |
| EXPERIMENTAL = no | |
| LIBRARY TYPE = dynamic |
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| [INFO] Scanning for projects... | |
| [INFO] ------------------------------------------------------------------------ | |
| [INFO] Reactor Build Order: | |
| [INFO] | |
| [INFO] nd4j | |
| [INFO] nd4j-shade | |
| [INFO] jackson | |
| [INFO] nd4j-common | |
| [INFO] nd4j-context | |
| [INFO] nd4j-buffer |
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| package com.tactico.tm.nn.graphs.confs; | |
| import org.deeplearning4j.api.storage.StatsStorage; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.BackpropType; | |
| import org.deeplearning4j.nn.conf.ComputationGraphConfiguration; | |
| import org.deeplearning4j.nn.conf.NeuralNetConfiguration; | |
| import org.deeplearning4j.nn.conf.Updater; | |
| import org.deeplearning4j.nn.conf.inputs.InputType; | |
| import org.deeplearning4j.nn.conf.layers.GravesLSTM; |
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| package com.tactico.tm.nn.graphs; | |
| import java.io.File; | |
| import java.io.FileNotFoundException; | |
| import java.io.IOException; | |
| import org.deeplearning4j.api.storage.StatsStorage; | |
| import org.deeplearning4j.nn.api.Layer; | |
| import org.deeplearning4j.nn.api.OptimizationAlgorithm; | |
| import org.deeplearning4j.nn.conf.BackpropType; |
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| #from scipy.linalg import cho_solve | |
| from scipy.linalg.lapack import dpotrs | |
| import numpy as np | |
| def choBackSubstitution(L,y,lower=True,modify=False): | |
| if not modify: | |
| x = np.copy(y) | |
| else: | |
| x = y | |
| if lower: |