| /dports/devel/spark/spark-2.1.1/mllib/src/test/scala/org/apache/spark/mllib/regression/ |
| H A D | LinearRegressionSuite.scala | 31 val model = new LinearRegressionModel(weights = Vectors.dense(0.1, 0.2, 0.3), intercept = 0.5) constant
52 val model = linReg.run(testRDD) constant
55 val weights = model.weights constant
78 val model = linReg.run(testRDD) constant
82 val weights = model.weights constant
109 val model = linReg.run(sparseRDD) constant
113 val weights = model.weights constant
135 val model = LinearRegressionSuite.model constant
163 val model = LinearRegressionWithSGD.train(points, 2) constant
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| /dports/devel/spark/spark-2.1.1/mllib/src/test/java/org/apache/spark/mllib/regression/ |
| H A D | JavaLinearRegressionSuite.java | 33 int validatePrediction(List<LabeledPoint> validationData, LinearRegressionModel model) { in validatePrediction()
49 double[] weights = {10, 10}; in runLinearRegressionUsingConstructor() local
58 LinearRegressionModel model = linSGDImpl.run(testRDD.rdd()); in runLinearRegressionUsingConstructor() local
68 double[] weights = {10, 10}; in runLinearRegressionUsingStaticMethods() local
75 LinearRegressionModel model = LinearRegressionWithSGD.train(testRDD.rdd(), 100); in runLinearRegressionUsingStaticMethods() local
85 double[] weights = {10, 10}; in testPredictJavaRDD() local
89 LinearRegressionModel model = linSGDImpl.run(testRDD.rdd()); in testPredictJavaRDD() local
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| H A D | JavaLassoSuite.java | 31 int validatePrediction(List<LabeledPoint> validationData, LassoModel model) { in validatePrediction()
47 double[] weights = {-1.5, 1.0e-2}; in runLassoUsingConstructor() local
58 LassoModel model = lassoSGDImpl.run(testRDD.rdd()); in runLassoUsingConstructor() local
68 double[] weights = {-1.5, 1.0e-2}; in runLassoUsingStaticMethods() local
75 LassoModel model = LassoWithSGD.train(testRDD.rdd(), 100, 1.0, 0.01, 1.0); in runLassoUsingStaticMethods() local
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| /dports/graphics/opencv/opencv-4.5.3/modules/gapi/src/backends/ie/ |
| H A D | bindings_ie.cpp | 4 const std::string &model, in PyParams()
5 const std::string &weights, in PyParams()
11 const std::string &model, in PyParams()
29 const std::string &model, in params()
30 const std::string &weights, in params()
36 const std::string &model, in params()
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| /dports/devel/spark/spark-2.1.1/mllib/src/test/java/org/apache/spark/mllib/classification/ |
| H A D | JavaSVMSuite.java | 31 int validatePrediction(List<LabeledPoint> validationData, SVMModel model) { in validatePrediction()
46 double[] weights = {-1.5, 1.0}; in runSVMUsingConstructor() local
58 SVMModel model = svmSGDImpl.run(testRDD.rdd()); in runSVMUsingConstructor() local
68 double[] weights = {-1.5, 1.0}; in runSVMUsingStaticMethods() local
75 SVMModel model = SVMWithSGD.train(testRDD.rdd(), 100, 1.0, 1.0, 1.0); in runSVMUsingStaticMethods() local
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| /dports/math/mlpack/mlpack-3.4.2/src/mlpack/tests/main_tests/ |
| H A D | decision_tree_test.cpp | 67 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
113 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
157 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
188 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
218 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
248 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
305 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
371 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
380 DecisionTreeModel* model = IO::GetParam<DecisionTreeModel*>("output_model"); variable
409 arma::mat weights(1, labels.n_cols, arma::fill::ones); variable
[all …]
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| /dports/games/leela-zero/leela-zero-0.17/training/minigo/ |
| H A D | convert_minigo.py | 21 def getMinigoWeightsV1(model): argument
53 def getMinigoWeightsV2(model): argument
132 def merge_gammas(weights): argument
175 def save_leelaz_weights(filename, weights): argument
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| /dports/astro/py-astropy/astropy-5.0/astropy/modeling/ |
| H A D | fitting.py | 172 def wrapper(self, model, x, y, z=None, **kwargs): argument
455 def _map_domain_window(self, model, x, y=None): argument
482 def __call__(self, model, x, y, z=None, weights=None, rcond=None): argument
846 def __call__(self, model, x, y, z=None, weights=None, **kwargs): argument
1099 def __call__(self, model, x, y, z=None, weights=None, argument
1185 def _wrap_deriv(params, model, weights, x, y, z=None): argument
1268 def __call__(self, model, x, y, z=None, weights=None, **kwargs): argument
1340 def __call__(self, model, x, y, z=None, weights=None, **kwargs): argument
1596 def _fitter_to_model_params(model, fps): argument
1659 def _model_to_fit_params(model): argument
[all …]
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| /dports/devel/spark/spark-2.1.1/mllib/src/main/scala/org/apache/spark/ml/ann/ |
| H A D | Layer.scala | 88 val weights: BDV[Double] constant
154 val weights: BDV[Double], constant
319 val weights = new BDV[Double](0) constant
340 def model(weights: Vector): TopologyModel method
341 def model(seed: Long): TopologyModel method
349 val weights: Vector constant
452 val weights: Vector, constant
564 val weights = BDV.zeros[Double](topology.layers.map(_.weightSize).sum) constant
589 val model = topology.model(weights) constant
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| /dports/math/R-cran-lava/lava/R/ |
| H A D | score.R | 8 score.lvm <- function(x, data, p, model="gaussian", S, n, mu=NULL, weights=NULL, data2=NULL, debug=… argument
90 weights=Weights(x$estimate), argument
97 …S <- do.call("score",c(list(x=x$estimate$model0,p=p, model=estimator, weights=weights, data2=data2… nameattr
121 score.multigroupfit <- function(x,p=pars(x), weights=Weights(x), estimator=x$estimator, data2=x$dat… argument
130 score.multigroup <- function(x,data=x$data,weights=NULL,data2=NULL,p,indiv=combine,combine=FALSE,..… argument
158 score.lvmfit <- function(x, data=model.frame(x), p=pars(x), model=x$estimator, weights=Weights(x), … argument
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| H A D | logLik.R | 4 logLik.lvm <- function(object,p,data,model="gaussian",indiv=FALSE,S,mu,n,debug=FALSE,weights=NULL,d… argument
101 …loglik <- do.call(lname, c(list(object=object,p=p,data=data,indiv=indiv,weights=weights,data2=data… nameattr
138 … weights=NULL, indiv=FALSE, S, mu, n, offset=NULL, debug=FALSE, meanstructure=TRUE,...) { argument
272 data=model.frame(object), argument
274 weights=Weights(object), argument
294 p=pars(object), model=object$estimator, argument
295 weights=Weights(object$estimate), argument
305 logLik.multigroup <- function(object,p,data=object$data,weights=NULL,type=c("cond","sim","exo","sat… argument
346 p=pars(object), weights=Weights(object), model=object$estimator, ...) { argument
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| /dports/audio/sphinxbase/sphinxbase-0.8/test/unit/test_ngram/ |
| H A D | test_lm_class.c | 12 run_tests(logmath_t *lmath, ngram_model_t *model) in run_tests()
91 float32 weights[] = { 0.6, 0.4 }; in run_tests() local
108 ngram_model_t *model; in main() local
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| /dports/math/R-cran-MatchIt/MatchIt/R/ |
| H A D | distance2_methods.R | 18 return(list(model = res, distance = pred)) nameattr
32 weights <- A$weights functionVar
36 weights = weights), A), nameattr
63 weights <- A$weights functionVar
66 …res <- do.call(nnet::nnet, c(list(formula, data, weights = weights, entropy = TRUE), A), quote = T… nameattr
110 return(list(model = res, distance = pred)) nameattr
132 return(list(model = res, distance = pred)) nameattr
218 return(list(model = res, distance = pred)) nameattr
250 return(list(model = res, distance = pred)) nameattr
282 return(list(model = res, distance = pred)) nameattr
[all …]
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| /dports/biology/biosig/biosig-2.3.3/biosig4matlab/t400_Classification/ |
| H A D | train_sc.m | 302 weights = sparse(length(cix)+1,M); variable
314 weights = weights + alpha * [1,D(k,cix)]' * e ; variable
324 weights = weights + W(k) * [1,D(k,cix)]' * e ; variable
338 weights = sparse(length(cix)+1,M); variable
360 weights = weights + W(k) * [1,D(k,cix)]' * e ; variable
377 weights = ones(length(cix),M); variable
382 weights = weights.* 2.^(D(k,cix)' * e); variable
638 … model = train(W, classlabel, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, variable
639 weights = model.w([end,1:end-1],:)'; variable
698 … model = train(W, cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, variable
[all …]
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| /dports/math/octave-forge-nan/nan-3.6.1/inst/ |
| H A D | train_sc.m | 308 weights = sparse(length(cix)+1,M); variable
320 weights = weights + alpha * [1,D(k,cix)]' * e ; variable
330 weights = weights + W(k) * [1,D(k,cix)]' * e ; variable
344 weights = sparse(length(cix)+1,M); variable
366 weights = weights + W(k) * [1,D(k,cix)]' * e ; variable
383 weights = ones(length(cix),M); variable
388 weights = weights.* 2.^(D(k,cix)' * e); variable
645 … model = train(classlabel, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, variable
646 weights = model.w([end,1:end-1],:)'; variable
707 … model = train(cl, sparse(D), CC.options); % C-SVC, C=1, linear kernel, degree = 1, variable
[all …]
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| /dports/graphics/opencv/opencv-4.5.3/contrib/modules/mcc/test/ |
| H A D | test_ccm.cpp | 41 ColorCorrectionModel model(s / 255, COLORCHECKER_Macbeth); in TEST() local
125 Mat weights = (Mat_<double>(8, 1) << in TEST() local
150 Mat weights = (Mat_<double>(20, 1) << in TEST() local
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| /dports/graphics/opencv/opencv-4.5.3/modules/video/test/ |
| H A D | test_trackers.cpp | 56 std::string model = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.prototxt"); in TEST_P() local
57 std::string weights = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.caffemodel", false); in TEST_P() local
71 std::string model = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.prototxt"); in TEST() local
72 std::string weights = cvtest::findDataFile("dnn/gsoc2016-goturn/goturn.caffemodel", false); in TEST() local
101 std::string model = cvtest::findDataFile("dnn/onnx/models/dasiamrpn_model.onnx", false); in TEST() local
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| /dports/science/lammps/lammps-stable_29Sep2021/examples/mliap/ |
| H A D | convert_mliap_Ta06A.py | 13 weights = coeffs[1:] variable
21 model = IgnoreElems(lin) # The linear module does not use the types. variable
|
| /dports/math/py-keras/Keras-2.4.3/keras/utils/ |
| H A D | layer_utils.py | 12 def count_params(weights): argument
30 def convert_all_kernels_in_model(model): argument
|
| /dports/www/chromium-legacy/chromium-88.0.4324.182/components/assist_ranker/ |
| H A D | quantized_nn_classifier_unittest.cc | 19 const vector<vector<int>>& weights, in CreateLayer()
41 QuantizedNNClassifierModel model; in CreateModel() local
65 const NNClassifierModel model = Dequantize(quantized); in TEST() local
97 const NNClassifierModel model = Dequantize(quantized); in TEST() local
108 QuantizedNNClassifierModel model; in TEST() local
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| H A D | nn_classifier_test_util.cc | 17 const vector<vector<float>>& weights, in CreateLayer()
34 NNClassifierModel model; in CreateModel() local
40 bool CheckInference(const NNClassifierModel& model, in CheckInference()
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| /dports/www/qt5-webengine/qtwebengine-everywhere-src-5.15.2/src/3rdparty/chromium/components/assist_ranker/ |
| H A D | quantized_nn_classifier_unittest.cc | 20 const vector<vector<int>>& weights, in CreateLayer()
42 QuantizedNNClassifierModel model; in CreateModel() local
66 const NNClassifierModel model = Dequantize(quantized); in TEST() local
98 const NNClassifierModel model = Dequantize(quantized); in TEST() local
109 QuantizedNNClassifierModel model; in TEST() local
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| H A D | nn_classifier_test_util.cc | 17 const vector<vector<float>>& weights, in CreateLayer()
34 NNClassifierModel model; in CreateModel() local
40 bool CheckInference(const NNClassifierModel& model, in CheckInference()
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| /dports/www/gohugo/hugo-0.91.2/vendor/github.com/jdkato/prose/scripts/ |
| H A D | update_model.py | 11 def dump_model(model, data): argument
19 weights, tagdict, classes = w_td_c variable
|
| /dports/misc/mmdnn/MMdnn-0.3.1/mmdnn/conversion/pytorch/ |
| H A D | torch_to_np.py | 4 model = torchfile.load('kit.model') variable
6 weights = dict() variable
|