/dports/devel/R-cran-ModelMetrics/ModelMetrics/R/ |
H A D | RcppExports.R | 8 auc_ <- function(actual, predicted) { argument 12 auc2_ <- function(actual, predicted) { argument 16 auc3_ <- function(actual, predicted, ranks) { argument 48 brier_ <- function(actual, predicted) { argument 60 mae_ <- function(actual, predicted) { argument 64 ce_ <- function(actual, predicted) { argument 68 mse_ <- function(actual, predicted) { argument 72 msle_ <- function(actual, predicted) { argument 76 rmsle_ <- function(actual, predicted) { argument 80 rmse_ <- function(actual, predicted) { argument [all …]
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H A D | ModelMetrics.R | 21 mlogLoss <- function(actual, predicted){ argument 27 predicted = as.matrix(predicted) 31 predicted = pmax(pmin(predicted, 1 - eps), eps) 33 mlogLoss_(actual, predicted) 64 mauc <- function(actual, predicted){ argument 121 ppv_(actual, predicted, cutoff) 127 ppv_(actual, predicted, cutoff) 151 npv_(actual, predicted, cutoff) 213 tnr_(actual, predicted, cutoff) 218 tnr_(actual, predicted, cutoff) [all …]
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H A D | rmsle.R | 18 rmsle_(actual, predicted) 25 predicted <- modelObject$fitted.values functionVar 28 rmsle.default(actual, predicted) 44 rmsle.default(actual, predicted) 54 rmsle.default(actual, predicted) 62 predicted <- modelObject@resp$mu functionVar 64 rmsle.default(actual, predicted) 72 predicted <- modelObject$fit functionVar 74 rmsle.default(actual, predicted) 82 predicted <- predict(modelObject) functionVar [all …]
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H A D | ce.R | 18 ce_(actual, predicted) 25 predicted <- modelObject$fitted.values functionVar 28 ce.default(actual, predicted) 44 ce.default(actual, predicted) 54 ce.default(actual, predicted) 62 predicted <- modelObject@resp$mu functionVar 64 ce.default(actual, predicted) 72 predicted <- modelObject$fit functionVar 74 ce.default(actual, predicted) 82 predicted <- predict(modelObject) functionVar [all …]
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H A D | msle.R | 18 msle_(actual, predicted) 25 predicted <- modelObject$fitted.values functionVar 28 msle.default(actual, predicted) 44 msle.default(actual, predicted) 54 msle.default(actual, predicted) 62 predicted <- modelObject@resp$mu functionVar 64 msle.default(actual, predicted) 72 predicted <- modelObject$fit functionVar 74 msle.default(actual, predicted) 82 predicted <- predict(modelObject) functionVar [all …]
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H A D | logLoss.R | 31 predicted = pmax(pmin(predicted, 1 - eps), eps) 35 return(logLoss_(actual, predicted)) 39 return(plogLoss_(actual, predicted)) 54 predicted <- modelObject$fitted.values functionVar 70 logLoss.default(actual, predicted) 78 predicted <- modelObject@resp$mu functionVar 80 logLoss.default(actual, predicted) 88 predicted <- modelObject$fit functionVar 90 logLoss.default(actual, predicted) 98 predicted <- predict(modelObject) functionVar [all …]
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H A D | mse.R | 25 mse_(actual, predicted) 32 predicted <- modelObject$fitted.values functionVar 33 actual <- modelObject$residuals + predicted 35 mse.default(actual, predicted) 45 predicted <- modelObject$fitted.values functionVar 50 mse.default(actual, predicted) 79 rmse_(actual, predicted) 86 predicted <- modelObject$fitted.values functionVar 89 rmse.default(actual, predicted) 99 predicted <- modelObject$fitted.values functionVar [all …]
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H A D | gini.R | 30 df1 <- data.frame(actual = actual, predicted = predicted) nameattr 32 df1 <- df1[order(-df1$predicted),] 45 predicted <- modelObject$fitted.values functionVar 50 gini.default(actual, predicted) 61 gini.default(actual, predicted) 69 predicted <- modelObject@resp$mu functionVar 71 gini.default(actual, predicted) 79 predicted <- modelObject$fit functionVar 81 gini.default(actual, predicted) 89 predicted <- predict(modelObject) functionVar [all …]
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H A D | auc.R | 40 ranks = frankv(predicted) 41 AUC <- auc3_(actual, predicted, ranks) 43 AUC <- auc_(actual, predicted) 60 auc.default(actual, predicted) 70 auc.default(actual, predicted) 78 predicted <- modelObject@resp$mu functionVar 80 auc.default(actual, predicted) 88 predicted <- modelObject$fit functionVar 90 auc.default(actual, predicted) 98 predicted <- predict(modelObject) functionVar [all …]
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H A D | mae.R | 17 mae.default <- function(actual, predicted, ...){ argument 18 mae_(actual, predicted) 29 predicted <- modelObject$fitted.values functionVar 34 mae.default(actual, predicted) 44 mae.default(actual, predicted) 52 predicted <- modelObject@resp$mu functionVar 54 mae.default(actual, predicted) 62 predicted <- modelObject$fit functionVar 64 mae.default(actual, predicted) 72 predicted <- predict(modelObject) functionVar [all …]
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H A D | brier.R | 17 brier.default <- function(actual, predicted, ...){ argument 18 brier_(actual, predicted) 29 predicted <- modelObject$fitted.values functionVar 34 brier.default(actual, predicted) 44 brier.default(actual, predicted) 52 predicted <- modelObject@resp$mu functionVar 54 brier.default(actual, predicted) 62 predicted <- modelObject$fit functionVar 64 brier.default(actual, predicted) 72 predicted <- predict(modelObject) functionVar [all …]
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/dports/graphics/blender/blender-2.91.0/intern/libmv/libmv/autotrack/ |
H A D | predict_tracks_test.cc | 56 Marker predicted; in TEST() local 57 predicted.clip = 0; in TEST() 58 predicted.track = 0; in TEST() 59 predicted.frame = 8; in TEST() 90 Marker predicted; in TEST() local 91 predicted.clip = 0; in TEST() 96 LG << predicted; in TEST() 125 Marker predicted; in TEST() local 144 Marker predicted; in TEST() local 166 Marker predicted; in TEST() local [all …]
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/dports/devel/R-cran-ModelMetrics/ModelMetrics/src/ |
H A D | confusionMatrix_.cpp | 10 cMat(0,0) = sum((predicted <= cutoff) & (actual == 0)); in confusionMatrix_() 12 cMat(0,1) = sum((predicted <= cutoff) & (actual == 1)); in confusionMatrix_() 14 cMat(1,0) = sum((predicted > cutoff) & (actual == 0)); in confusionMatrix_() 16 cMat(1,1) = sum((predicted > cutoff) & (actual == 1)); in confusionMatrix_() 57 double TN = sum((predicted < cutoff) & (actual == 0)); in tnr_() 81 double p = ppv_(actual, predicted, cutoff); in fScore_() 82 double r = recall_(actual, predicted, cutoff); in fScore_() 96 double p = ppv_(actual, predicted, cutoff); in f1Score_() 97 double r = recall_(actual, predicted, cutoff); in f1Score_() 111 double brier = mean(pow(actual - predicted, 2)); in brier_() [all …]
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H A D | RcppExports.cpp | 20 double auc_(NumericVector actual, NumericVector predicted); 27 rcpp_result_gen = Rcpp::wrap(auc_(actual, predicted)); in _ModelMetrics_auc_() 32 double auc2_(NumericVector actual, NumericVector predicted); 39 rcpp_result_gen = Rcpp::wrap(auc2_(actual, predicted)); in _ModelMetrics_auc2_() 187 double mae_(NumericVector actual, NumericVector predicted); 194 rcpp_result_gen = Rcpp::wrap(mae_(actual, predicted)); in _ModelMetrics_mae_() 199 double ce_(NumericVector actual, NumericVector predicted); 206 rcpp_result_gen = Rcpp::wrap(ce_(actual, predicted)); in _ModelMetrics_ce_() 211 double mse_(NumericVector actual, NumericVector predicted); 218 rcpp_result_gen = Rcpp::wrap(mse_(actual, predicted)); in _ModelMetrics_mse_() [all …]
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H A D | error.cpp | 5 double mae_(NumericVector actual, NumericVector predicted) { in mae_() argument 6 double mae = mean(abs(actual - predicted)); in mae_() 12 double ce_(NumericVector actual, NumericVector predicted) { in ce_() argument 14 double Rows = predicted.size(); in ce_() 18 if(actual(i) != predicted(i)) { in ce_() 30 double mse_(NumericVector actual, NumericVector predicted) { in mse_() argument 32 NumericVector err = (actual-predicted); in mse_() 40 double msle_(NumericVector actual, NumericVector predicted) { in msle_() argument 54 double rmsle = sqrt(msle_(actual, predicted)); in rmsle_() 61 double rmse_(NumericVector actual, NumericVector predicted) { in rmse_() argument [all …]
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H A D | logLoss_.cpp | 5 double logLoss_(NumericVector actual, NumericVector predicted) { in logLoss_() argument 7 NumericVector ll = -1*(actual*log(predicted) + (1-actual)*log(1-predicted)); in logLoss_() 15 double mlogLoss_(NumericVector actual, NumericMatrix predicted) { in mlogLoss_() argument 17 double Rows = predicted.nrow(); in mlogLoss_() 18 double Cols = predicted.ncol(); in mlogLoss_() 26 double mlogloss = (-1 / Rows) * sum(actualMat * log(predicted)); in mlogLoss_() 33 double plogLoss_(NumericVector actual, NumericVector predicted) { in plogLoss_() argument 35 NumericVector pl = log(gamma(actual + 1)) + predicted - log(predicted) * actual; in plogLoss_()
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/dports/math/py-networkx/networkx-2.6.3/networkx/algorithms/node_classification/tests/ |
H A D | test_harmonic_function.py | 16 predicted = node_classification.harmonic_function(G, label_name=label_name) 17 assert predicted[0] == "A" 18 assert predicted[1] == "A" 19 assert predicted[2] == "B" 20 assert predicted[3] == "B" 55 assert predicted[0] == "A" 56 assert predicted[1] == "A" 57 assert predicted[2] == "A" 58 assert predicted[3] == "A" 65 assert predicted[i] == G.nodes[i][label_name] [all …]
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H A D | test_local_and_global_consistency.py | 17 predicted = node_classification.local_and_global_consistency( 20 assert predicted[0] == "A" 21 assert predicted[1] == "A" 22 assert predicted[2] == "B" 23 assert predicted[3] == "B" 57 predicted = node_classification.local_and_global_consistency( 60 assert predicted[0] == "A" 61 assert predicted[1] == "A" 62 assert predicted[2] == "A" 63 assert predicted[3] == "A" [all …]
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/dports/devel/R-cran-caret/caret/R/ |
H A D | train_recipes.R | 24 predicted <- predicted[, object$levels] 26 predicted 301 predicted <- lapply(predicted, 312 for(k in seq(along = predicted)) predicted[[k]] <- 315 predicted <- do.call("rbind", predicted) 318 predicted <- cbind(predicted, allParam) 491 predicted <- trim_values(predicted, ctrl, is.null(lev)) 505 predicted <- lapply(predicted, 767 predicted <- lapply(predicted, 945 predicted <- lapply(predicted, [all …]
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H A D | adaptive.R | 94 predicted[seq(along = predicted)] <- NA 101 for(i in seq(along = predicted)) predicted[[i]] <- tmp 120 predicted[seq(along = predicted)] <- NA 127 for(i in seq(along = predicted)) predicted[[i]] <- tmp 164 predicted <- lapply(predicted, 178 … for(k in seq(along = predicted)) predicted[[k]] <- cbind(predicted[[k]], probValues[[k]]) 389 predicted <- lapply(predicted, 607 predicted[seq(along = predicted)] <- NA 633 predicted[seq(along = predicted)] <- NA 678 predicted <- lapply(predicted, [all …]
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/dports/science/PETSc/petsc-3.14.1/src/tao/tutorials/output/ |
H A D | ex4_0.out | 1 J(xhat): 24.5106, predicted: 0.851231, diff 23.6594 2 J(xhat): 6.76613, predicted: 0.851288, diff 5.91484 3 J(xhat): 2.33003, predicted: 0.851317, diff 1.47871 4 J(xhat): 1.22101, predicted: 0.851331, diff 0.369677 5 J(xhat): 0.943758, predicted: 0.851338, diff 0.0924194 6 J(xhat): 0.874447, predicted: 0.851342, diff 0.0231048 7 J(xhat): 0.85712, predicted: 0.851344, diff 0.00577621 8 J(xhat): 0.852789, predicted: 0.851344, diff 0.00144405 9 J(xhat): 0.851706, predicted: 0.851345, diff 0.000361013 10 J(xhat): 0.851435, predicted: 0.851345, diff 9.02533e-05
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H A D | ex4_lmvm_admm_2.out | 8 J(xhat): 98.3926, predicted: 4.22724, diff 94.1653 9 J(xhat): 27.7686, predicted: 4.22724, diff 23.5413 10 J(xhat): 10.1126, predicted: 4.22724, diff 5.88533 11 J(xhat): 5.69858, predicted: 4.22724, diff 1.47133 12 J(xhat): 4.59508, predicted: 4.22724, diff 0.367833 13 J(xhat): 4.3192, predicted: 4.22724, diff 0.0919583 14 J(xhat): 4.25023, predicted: 4.22724, diff 0.0229896 15 J(xhat): 4.23299, predicted: 4.22724, diff 0.0057474 16 J(xhat): 4.22868, predicted: 4.22724, diff 0.00143685 17 J(xhat): 4.2276, predicted: 4.22724, diff 0.000359212
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H A D | ex4_nm_admm_2.out | 8 J(xhat): 98.3842, predicted: 4.21886, diff 94.1653 9 J(xhat): 27.7644, predicted: 4.22306, diff 23.5413 10 J(xhat): 10.1105, predicted: 4.22516, diff 5.88533 11 J(xhat): 5.69755, predicted: 4.22621, diff 1.47133 12 J(xhat): 4.59457, predicted: 4.22674, diff 0.367833 13 J(xhat): 4.31896, predicted: 4.227, diff 0.0919583 14 J(xhat): 4.25012, predicted: 4.22713, diff 0.0229896 15 J(xhat): 4.23295, predicted: 4.2272, diff 0.0057474 16 J(xhat): 4.22867, predicted: 4.22723, diff 0.00143685 17 J(xhat): 4.22761, predicted: 4.22725, diff 0.000359212
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/dports/devel/sdl20/SDL2-2.0.18/test/ |
H A D | testautomation_stdlib.c | 47 int predicted; in stdlib_snprintf() local 74 predicted = SDL_snprintf(NULL, 0, "%f", 0.0); in stdlib_snprintf() 79 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 87 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 95 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 103 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 111 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 119 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 127 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 135 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() [all …]
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/dports/x11/controllermap/SDL2-2.0.18/test/ |
H A D | testautomation_stdlib.c | 47 int predicted; in stdlib_snprintf() local 74 predicted = SDL_snprintf(NULL, 0, "%f", 0.0); in stdlib_snprintf() 79 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 87 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 95 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 103 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 111 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 119 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 127 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() 135 …SDLTest_AssertCheck(predicted == result, "Check predicted value, expected: %d, got: %d", result, p… in stdlib_snprintf() [all …]
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