/dports/math/R-cran-ddalpha/ddalpha/R/ |
H A D | dataf.sim.r | 13 cov[i,j] <- 0.2*exp(-abs(t[i] - t[j])/0.3) 31 class(learn) = "functional" 42 if (learn$labels[[i]] == 0){ 46 if (learn$labels[[i]] == 1){ 50 lines(learn$dataf[[i]]$args, learn$dataf[[i]]$vals, col=lineColor, lty=lineType) 54 return (list(learn = learn, test = test)) nameattr 64 mean0 <- 30*(1 - t)*t^2 + 0.5*abs(sin(20*pi*t)) 68 cov[i,j] <- 0.2*exp(-abs(t[i] - t[j])/0.3) 89 class(learn) = "functional" 109 lines(learn$dataf[[i]]$args, learn$dataf[[i]]$vals, col=lineColor, lty=lineType) [all …]
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/dports/math/vowpal_wabbit/vowpal_wabbit-7.10/python/ |
H A D | test.py | 11 if abs(a-b) > eps: 12 …+ str(a) + " and " + str(b) + " to be " + str(eps) + "-close, but they differ by " + str(abs(a-b))) 19 vw.learn("1 |x a b") 25 ex.learn() ; ex.learn() ; ex.learn() ; ex.learn() 50 ex.learn() ; ex.learn() ; ex.learn() ; ex.learn() 78 ex.learn() 96 ex.learn() ; ex.learn() ; ex.learn() ; ex.learn() 105 ex.learn()
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/dports/math/R-cran-ddalpha/ddalpha/man/ |
H A D | dataf.sim.2.CFF07.Rd | 10 X(t) = m_0(t) + e(t), m_0(t) = 30*(1-t)*t^2 + 0.5*abs(sin(20*pi*t)) \cr 12 e(t): Gaussian with mean = 0, cov(X(s), X(t)) = 0.2*exp(-abs(s - t)/0.3)\cr 35 A data strusture containing \code{$learn} and \code{$test} functional data. 62 learn = dataf$learn 66 unique(learn$labels) 69 learn$dataf[[2]]$args[5] 70 learn$dataf[[2]]$vals[5] 74 plot(learn)
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H A D | dataf.sim.1.CFF07.Rd | 12 e(t): Gaussian with mean = 0, cov(X(s), X(t)) = 0.2*exp(-abs(s - t)/0.3)\cr 36 A data strusture containing \code{$learn} and \code{$test} functional data. 63 learn = dataf$learn 67 unique(learn$labels) 70 learn$dataf[[2]]$args[5] 71 learn$dataf[[2]]$vals[5] 75 plot(learn)
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/dports/science/pybrain/pybrain-0.3.3/examples/optimization/ |
H A D | optimizerinterface.py | 79 l.learn(0) 84 l.learn(0) 108 print(l.learn(5)) 117 l.learn() 118 print(l.learn(), 'in', l.numEvaluations, 'evaluations.') 122 print(l.learn(), ': fitness below 10 (we minimize the function).') 126 l.learn() 127 print(l.learn(), 'in', l.numLearningSteps, 'learning steps.') 131 print(l.learn(75), 'in', l.numLearningSteps, 'total learning steps.') 136 l.learn() [all …]
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/dports/science/pybrain/pybrain-0.3.3/pybrain/tools/ |
H A D | aptativeresampling.py | 56 res = l.learn() 58 pylab.plot(list(map(abs, l._allEvaluations))) 61 res = l2.learn() 64 pylab.plot(list(map(abs,l2._allEvaluations)))
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/dports/misc/orange3/orange3-3.29.1/Orange/tests/ |
H A D | test_softmax_regression.py | 37 learn = SoftmaxRegressionLearner() 38 clf = learn(self.iris) 40 self.assertLess(abs(p.sum(axis=1) - 1).all(), 1e-6)
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H A D | test_logistic_regression.py | 22 learn = LogisticRegressionLearner() 24 results = cv(self.heart_disease, [learn]) 61 learn = LogisticRegressionLearner(penalty='l1') 62 clf = learn(self.iris[:100]) 64 self.assertLess(abs(p.sum(axis=1) - 1).all(), 1e-6) 110 learn = LogisticRegressionLearner() 111 model = learn(self.heart_disease) 125 learn = LogisticRegressionLearner() 127 learn(t)
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H A D | test_linear_regression.py | 35 learn = LinearRegressionLearner() 36 clf = learn(t) 38 self.assertTrue((abs(z.reshape(-1, 1) - y2) < 2.0).all())
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/dports/science/pybrain/pybrain-0.3.3/pybrain/tools/mixtures/ |
H A D | mogpuremax.py | 42 if abs(x) >= 4.0: return 0.000000001 64 def learn(self, x, y): member in MixtureOfGaussians 105 if abs(n) > 5.0: n = 0.0 107 m.learn(x, y)
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H A D | mixtureofgaussian.py | 40 if abs(x) >= 4.0: return 0.000000001 71 if abs(n) > 5.0: n = 0.0 94 def learn(self, x, y, dm="max", typ="logLiklihood"): member in MixtureOfGaussians 151 self.learn(sampleX, sampleY, dm, "logLiklihood")
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/dports/science/py-skrebate/skrebate-0.62/ |
H A D | README.md | 15 This package includes a scikit-learn-compatible Python implementation of ReBATE, a suite of [Relief… 35 …E software releases, this scikit-learn compatible version primarily focuses on ease of incorporati… 36 This code is most appropriate for scikit-learn users, Windows operating system users, beginners, or… 54 * scikit-learn 58 NumPy, SciPy, and scikit-learn can be installed in Anaconda via the command: 61 conda install numpy scipy scikit-learn 74 … into scikit-learn machine learning workflows. For example, the ReliefF algorithm can be used as a… 109 …). [Benchmarking Relief-Based Feature Selection Methods](https://arxiv.org/abs/1711.08477). *arXiv… 118 howpublished = {arXiv e-print. https://arxiv.org/abs/1711.08477},
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/dports/math/R-cran-CVST/CVST/R/ |
H A D | util.R | 63 constructLearner = function(learn, predict) { argument 64 stopifnot(is.function(learn) && is.function(predict)) 65 learner = list(learn=learn, predict=predict) nameattr 95 model = try(learner$learn(train, param)) 257 label = factor(y == abs(y)) 280 ret = t(sapply(x, function(xx) (1 + abs((xx - t) / w))^-4 )) %*% h
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/dports/games/scid/scid/engines/phalanx-scid/ |
H A D | phalanx.c | 73 { "learning", no_argument, &Flag.learn, 1 }, in main() 74 { "no-learning", no_argument, &Flag.learn, 0 }, in main() 127 Flag.learn = 0; in main() 156 Flag.resign = abs(i); } in main() 203 { case '+': Flag.learn = 1; break; in main() 204 case '-': Flag.learn = 0; break; in main() 260 { SizeHT = 0; Flag.ponder = 0; Flag.learn = 0; } in main() 298 Flag.post = Flag.book = Flag.learn = Flag.ponder = Flag.polling = 0; in main() 326 if( Flag.learn ) in main() 392 if( Learn.f == NULL && Flag.learn ) in main() [all …]
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/dports/math/R-cran-dimRed/dimRed/man/ |
H A D | UMAP-class.Rd | 58 package \code{umap-learn} (https://github.com/lmcinnes/umap/). This requires 59 \code{umap-learn} version 0.4 installed, at the time of writing, there is 60 already \code{umap-learn} 0.5 but it is not supported by the R package 70 \code{umap-learn} installed (use \code{pip install umap-learn}). 84 https://arxiv.org/abs/1802.03426
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/dports/games/phalanx/Phalanx-XXV/ |
H A D | phalanx.c | 101 { "learning", no_argument, &Flag.learn, 1 }, in main() 102 { "no-learning", no_argument, &Flag.learn, 0 }, in main() 158 Flag.learn = 0; in main() 187 Flag.resign = abs(i); } in main() 250 { case '+': Flag.learn = 1; break; in main() 251 case '-': Flag.learn = 0; break; in main() 308 Flag.learn = 0; Flag.ponder = 0; SizeHT = 0; in main() 313 if( Flag.random ) Flag.learn=0; in main() 386 if( Flag.learn ) in main() 452 if( Learn.f == NULL && Flag.learn ) in main() [all …]
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/dports/devel/py-bullet3/bullet3-3.21/examples/pybullet/gym/pybullet_envs/deep_mimic/mocap/ |
H A D | README.md | 7 If you want to learn movements directly from coordinates, you can use **inverse_kinect.py** and **r… 38 …Reinforcement Learning of Physics-Based Character Skills [[Link](https://arxiv.org/abs/1804.02717)] 40 … with temporal convolutions and semi-supervised training [[Link](https://arxiv.org/abs/1811.11742)]
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/dports/multimedia/termplay/termplay-2.0.6/cargo-crates/image-0.22.3/src/math/ |
H A D | nq.rs | 116 self.learn(pixels); in init() 202 dist = (n.b - b).abs(); in contest() 203 dist += (n.r - r).abs(); in contest() 205 dist += (n.g - g).abs(); in contest() 206 dist += (n.a - a).abs(); in contest() 228 fn learn(&mut self, pixels: &[u8]) { in learn() method
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/dports/www/geckodriver/mozilla-central-e9783a644016aa9b317887076618425586730d73/testing/geckodriver/cargo-crates/image-0.22.1/src/math/ |
H A D | nq.rs | 116 self.learn(pixels); in init() 202 dist = (n.b - b).abs(); in contest() 203 dist += (n.r - r).abs(); in contest() 205 dist += (n.g - g).abs(); in contest() 206 dist += (n.a - a).abs(); in contest() 228 fn learn(&mut self, pixels: &[u8]) { in learn() method
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/dports/shells/ion/ion-a8872014dbce730ccd00aaa722397dc394a52bf4/cargo-crates/image-0.21.2/src/math/ |
H A D | nq.rs | 116 self.learn(pixels); in init() 202 dist = (n.b - b).abs(); in contest() 203 dist += (n.r - r).abs(); in contest() 205 dist += (n.g - g).abs(); in contest() 206 dist += (n.a - a).abs(); in contest() 228 fn learn(&mut self, pixels: &[u8]) { in learn() method
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/dports/games/abstreet/abstreet-0.2.9-49-g74aca40c0/cargo-crates/image-0.23.4/src/math/ |
H A D | nq.rs | 116 self.learn(pixels); in init() 202 dist = (n.b - b).abs(); in contest() 203 dist += (n.r - r).abs(); in contest() 205 dist += (n.g - g).abs(); in contest() 206 dist += (n.a - a).abs(); in contest() 228 fn learn(&mut self, pixels: &[u8]) { in learn() method
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/dports/games/dose-response/dose-response-179c326/cargo-crates/image-0.20.1/src/math/ |
H A D | nq.rs | 116 self.learn(pixels); in init() 202 dist = (n.b - b).abs(); in contest() 203 dist += (n.r - r).abs(); in contest() 205 dist += (n.g - g).abs(); in contest() 206 dist += (n.a - a).abs(); in contest() 228 fn learn(&mut self, pixels: &[u8]) { in learn() method
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/dports/lang/spidermonkey78/firefox-78.9.0/third_party/rust/image/src/math/ |
H A D | nq.rs | 116 self.learn(pixels); in init() 202 dist = (n.b - b).abs(); in contest() 203 dist += (n.r - r).abs(); in contest() 205 dist += (n.g - g).abs(); in contest() 206 dist += (n.a - a).abs(); in contest() 228 fn learn(&mut self, pixels: &[u8]) { in learn() method
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/dports/graphics/viu/viu-1.1/cargo-crates/image-0.22.5/src/math/ |
H A D | nq.rs | 116 self.learn(pixels); in init() 202 dist = (n.b - b).abs(); in contest() 203 dist += (n.r - r).abs(); in contest() 205 dist += (n.g - g).abs(); in contest() 206 dist += (n.a - a).abs(); in contest() 228 fn learn(&mut self, pixels: &[u8]) { in learn() method
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/dports/multimedia/librav1e/rav1e-0.5.1/cargo-crates/color_quant-1.1.0/src/ |
H A D | lib.rs | 164 self.learn(pixels); in init() 278 dist = (n.b - b).abs(); in contest() 279 dist += (n.r - r).abs(); in contest() 281 dist += (n.g - g).abs(); in contest() 282 dist += (n.a - a).abs(); in contest() 304 fn learn(&mut self, pixels: &[u8]) { in learn() method
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