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6 | 6 |
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7 | 7 | import pymc3 as pm
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8 | 8 |
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9 |
| -__all__ = ['GPLatent', 'GPMarginal'] |
| 9 | +__all__ = ['GPLatent', 'GPMarginal', 'TProcess', 'GPMarginalSparse'] |
10 | 10 |
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11 | 11 | cholesky = pm.distributions.dist_math.Cholesky(nofail=True, lower=True)
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12 | 12 | solve_lower = tt.slinalg.Solve(A_structure='lower_triangular')
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@@ -42,8 +42,6 @@ def __call__(self, name, size, mean_func):
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42 | 42 | self.size = size
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43 | 43 | self.mean_func = mean_func
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44 | 44 |
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45 |
| - # help user keep track of GP since internal state changes |
46 |
| - self.call_record.append((name, size)) |
47 | 45 | # force user to run `__call__` before `conditioned_on`
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48 | 46 | self._ready = True
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49 | 47 | return self
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@@ -116,7 +114,7 @@ def _predictive_rv(self, X, f, Xs):
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116 | 114 |
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117 | 115 |
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118 | 116 |
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119 |
| -class StudentTProcess(GPLatent): |
| 117 | +class TProcess(GPLatent): |
120 | 118 | """ StudentT process
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121 | 119 | """
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122 | 120 | def __init__(self, cov_func):
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@@ -147,8 +145,6 @@ class GPMarginal(GPBase):
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147 | 145 |
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148 | 146 | def __init__(self, cov_func):
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149 | 147 | super(GPMarginal, self).__init__(cov_func)
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150 |
| - # if you want to predict or sample from noisy gp, |
151 |
| - # easy to add iid normal noise |
152 | 148 |
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153 | 149 | def __call__(self, name, size, mean_func, include_noise=False):
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154 | 150 | self.include_noise = include_noise
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