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Raw output: kernel_evaluations.c
+001: #!/usr/bin/env python
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002:
003: '''
004: Licensed to the Apache Software Foundation (ASF) under one or more
005: contributor license agreements. See the NOTICE file distributed with this
006: work for additional information regarding copyright ownership. The ASF
007: licenses this file to you under the Apache License, Version 2.0 (the
008: "License"); you may not use this file except in compliance with the
009: License. You may obtain a copy of the License at
010:
011: http://www.apache.org/licenses/LICENSE-2.0
012:
013: Unless required by applicable law or agreed to in writing, software
014: distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
015: WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
016: License for the specific language governing permissions and limitations
017: under the License.
018:
019: The code in this file was developed at Harvard University (2018) and
020: modified at ChemOS Inc. (2019) as stated in the NOTICE file.
021: '''
022:
+023: __author__ = 'Florian Hase'
if (PyDict_SetItem(__pyx_d, __pyx_n_s_author, __pyx_kp_s_Florian_Hase) < 0) __PYX_ERR(0, 23, __pyx_L1_error)
024:
025: #=========================================================================
026:
027: import cython
028: cimport cython
029:
030: from cython.parallel import prange
031:
+032: import numpy as np
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033: cimport numpy as np
034:
035: from libc.math cimport exp, abs, round
036:
037: #=========================================================================
038:
+039: cdef class KernelEvaluator:
struct __pyx_obj_8phoenics_15BayesianNetwork_18kernel_evaluations_KernelEvaluator {
PyObject_HEAD
struct __pyx_vtabstruct_8phoenics_15BayesianNetwork_18kernel_evaluations_KernelEvaluator *__pyx_vtab;
int num_samples;
int num_obs;
int num_kernels;
double lower_prob_bound;
double inv_vol;
PyArrayObject *np_locs;
PyArrayObject *np_sqrt_precs;
PyArrayObject *np_objs;
PyArrayObject *np_probs;
};
/* … */
struct __pyx_vtabstruct_8phoenics_15BayesianNetwork_18kernel_evaluations_KernelEvaluator {
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040:
041: cdef int num_samples, num_obs, num_kernels
042: cdef double lower_prob_bound, inv_vol
043:
044: cdef np.ndarray np_locs, np_sqrt_precs
045: cdef np.ndarray np_objs
046: cdef np.ndarray np_probs
047:
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for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) {
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__pyx_v_sample_index = __pyx_t_9;
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__pyx_t_10 = ((__pyx_v_kernel_index < __pyx_v_self->num_kernels) != 0);
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__pyx_t_10 = ((__pyx_v_sample_index == 0x64) != 0);
if (__pyx_t_10) {
/* … */
}
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/* … */
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static PyObject *__pyx_f_8phoenics_15BayesianNetwork_18kernel_evaluations_15KernelEvaluator_get_kernel(struct __pyx_obj_8phoenics_15BayesianNetwork_18kernel_evaluations_KernelEvaluator *__pyx_v_self, PyArrayObject *__pyx_v_sample, int __pyx_skip_dispatch) {
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double __pyx_v_temp_1;
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107:
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PyErr_SetString(PyExc_ZeroDivisionError, "float division");
__PYX_ERR(0, 126, __pyx_L1_error)
}
__pyx_v_inv_den = (1. / __pyx_t_11);
127:
+128: return num, inv_den, probs_sample
__Pyx_XDECREF(__pyx_r); __pyx_t_1 = PyFloat_FromDouble(__pyx_v_num); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 128, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); __pyx_t_2 = PyFloat_FromDouble(__pyx_v_inv_den); if (unlikely(!__pyx_t_2)) __PYX_ERR(0, 128, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_2); __pyx_t_3 = PyTuple_New(3); if (unlikely(!__pyx_t_3)) __PYX_ERR(0, 128, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_2); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_2); __Pyx_INCREF(__pyx_v_probs_sample); __Pyx_GIVEREF(__pyx_v_probs_sample); PyTuple_SET_ITEM(__pyx_t_3, 2, __pyx_v_probs_sample); __pyx_t_1 = 0; __pyx_t_2 = 0; __pyx_r = __pyx_t_3; __pyx_t_3 = 0; goto __pyx_L0;
129:
130:
131:
132:
133:
134: