Furmark 1 28 0 0

Author: g | 2025-04-25

Values: 0 to 3 - default is 0 -furmark-fur-img-id number set the image for the fur color. Values: 0 to 1 - default is 0 -furmark-dxt5 use compressed textures (DXT5) in FurMark stress test Values: 0 to 3 - default is 0 -furmark-fur-img-id number set the image for the fur color. Values: 0 to 1 - default is 0 -furmark-dxt5 use compressed textures (DXT5) in FurMark stress test

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Geeks3D FurMark 1 20 9 0 2025 11 28 09 08 57

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Collection of colors: &#10005dist = LearnDistribution[{RGBColor[0.5172966964096541, 0.4435322033449375, 1.], RGBColor[0.3984626930847484, 0.5592892024442906, 1.], RGBColor[0.6149389612362844, 0.5648721294502163, 1.], RGBColor[0.4129156497559272, 0.9146065592632544, 1.], RGBColor[0.7907065846445507, 0.41054133291260947`, 1.], RGBColor[0.4878854162550912, 0.9281119680196579, 1.], RGBColor[0.9884362181280959, 0.49025178842859785`, 1.], RGBColor[0.633242503827218, 0.9880985331612835, 1.], RGBColor[0.9215182482568276, 0.8103084921468551, 1.], RGBColor[0.667469513641223, 0.46420827644204676`, 1.]}]Once we have this “learned distribution”, we can do all sorts of things with it. For example, this generates 20 random samples from it: &#10005RandomVariate[dist,20]But now think about FindAnomalies. What it has to do is to find out which data points are anomalous relative to what’s expected. Or, in other words, given the underlying distribution of the data, it finds what data points are outliers, in the sense that they should occur only with very low probability according to the distribution.And just like for an ordinary numerical distribution, we can compute the PDF for a particular piece of data. Purple is pretty likely given the distribution of colors we’ve learned from our examples: &#10005PDF[dist, RGBColor[ 0.6323870562875563, 0.3525878887878987, 1.0002083564175581`]]But red is really really unlikely: &#10005PDF[dist, RGBColor[1, 0, 0]]For ordinary numerical distributions, there are concepts like CDF that tell us cumulative probabilities, say that we’ll get results that are “further out” than a particular value. For spaces of arbitrary things, there isn’t really a notion of “further out”. But we’ve come up with a function we call RarerProbability, that tells us what the total probability is of generating an example with a smaller PDF than something we give: &#10005RarerProbability[dist, RGBColor[ 0.6323870562875563, 0.3525878887878987, 1.0002083564175581`]] &#10005RarerProbability[dist, RGBColor[1, 0, 0]]Now we’ve got a way to describe anomalies: they’re just data points that have a very small rarer probability. And in fact FindAnomalies has an option AcceptanceThreshold (with default value 0.001) that specifies what should count as “very small”. OK, but let’s see this work on something more complicated than colors. Let’s train an anomaly detector by looking at 1000 examples of handwritten digits: &#10005AnomalyDetection[RandomSample[ResourceData["MNIST"][[All,1]],1000]]Now FindAnomalies can tell us which examples are anomalous: ✕FindAnomalies[AnomalyDetection[RandomSample[ResourceData["MNIST"][[All,1]],1000]], {\!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x+84O9URsb6P1ilPk1jAoLzWOUymJiEcchNY2Srm80kcAObHC9z1/8wJm9sUh0sWf+/2DItxyJ1T5Cp9f8tJqbDWOTmMgHlinDK8UpyMVn+xCL3K4iJEei7TdicAgT2jIyFOKT+5zGJ38YhtYiRtR6H1CtuRkNcJlozMa/BIfVYiMkAh9QjAyatF9gkrqo2GjDpPMeq6RzQ0zrPsBv4NI4p+AcuN1ITAABxtMfa"], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x/kgJGJcTUOqV4mFqY12KWKmBiZZI9jlwPqYsEu9ciKgYnRGrsuK6Au68e4dDEw4dbFVIpdFyNeu7D77NEqoC6mXhLt+n8Mt79C5XGGYhhuf4F14bALt7+OyeMKw///LYH+wi7z//9jayYWXHLUBgCB+cHS"], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x/M4I7MI1SBfL2vMOYpxsuocqGMd2DMLehyzoy9MKYTulwWYwuMKY0pdxanXCJCQJrtBqqcOWMXlPVLUhdVCmim76qdm+fNu76wktHr27dvyHLtjChAGFnuZbkTI6NiQIB/ABvDhOXn0Ez9+/37LxAtJPDsPy4gZIZT6gZnC065HYyncMr1IQIWAyQy+q/ELSd7FbfcBNxmir/DKUcVAADomc0b"], {{0, 28}, {28, 0}}, { 0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwUDwZwxxCnVOZfnHJ8T75rYAiKC4Cp2n+rMKRcXm8E099+GaNLsW/7OQ9E2/9ZhqFtwT8HEMV07J8RulT7v1WMINru3xsBNKmw339cQDTroX/FaFLSD/9NBzOU/n1gR5Mz+vdeBESz7P2XwsAujSIn+/zfxZychqO9//7dOHoRzX8xf/5BwN9fi/250ExVCwWC4n8//TE8BwW6/97ikmJI+HcLl5Tc43+TcUhxrP33uxmHnO+/P6W4jDz4bxNOl9AFAAAYpls0"], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x8UgKEep5QDbrn9DAz7SdC2vx6uDYtyGO2AKQU1CtO2/bAAxLStHqYa05FAKTBwwLRtPwMSwHQHg0M9RDu6bRAZCAPd/fX1cLPRtSGZjaENydr9uOTwpR88cvuxuBJJDqd19AAAMwi/NQ=="], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x+M4FkLV9+VK1fmZ2czqn5GlbotwwwETBDiOKrcPCYkuW1oZvZJOMWuBgJrZiaJ92hyPx6DqSkCzMInsTrokAwTk/AybDLH6oH2WWPR9emYPCczE1fjDwyZ9/tVwU6Uj9//BU3qoBIz3A+qaPbVMzExMjHJNU8p0hFgYij9jSy3Sl4t48CBVyDm1UIm5lcoGj8ignAquhwCXHHFDBeYq3CFy9srSUxMTJjhcvbYxn51kB+CMKSmcHGygPwnf/wzhpwbSIts8GrMIAO6gUktp+05DrdTEQAAo1CVcQ=="], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x/s4PcnHBKvNzqZCNX8wRD/8nmfPx8jEAjFo0udtVcAinNouWpp5aPLWTIysoauvIvNpq0cIhpJ2B1xgpvR7zsOKWFGRhxufyLEyLj5H1apz9ZAF0rkfsP01/8f9YwQEP0VTeZGqTojo+Xmfd0yjIy6P1HlUhk52yc+BrG6uTnPosolMDKKN4K93MDPuA1V7m0qMBA5tUr0tRgZ1dAt/L/SGOIUBqU3mA79sn9KhrOzc+0HrD4c3AAAH4+4UQ=="], {{0, 28}, {28, 0}}, {0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace -> Automatic, Interleaving -> None],Selectable->False],DefaultBaseStyle->"ImageGraphics",ImageSizeRaw->{28, 28},PlotRange->{{0, 28}, {0, 28}}]\), \!\(\*GraphicsBox[TagBox[RasterBox[CompressedData["1:eJxTTMoPSmNiYGAo5gASQYnljkVFiZXBAkBOaF5xZnpeaopnXklqemqRRRJImQwU/x+UoCeKkZGBkZGx4hmmHDsTFBg/wi3H1I0h186hbwkErExM2piGbnsPJN7xYpUDgr9vzUAWYkpcqK3NB9u3BUMqghXqlKq/6FKdMFfqY/qvFu4Fz1foclOYmTSNjAyx27ei/Or//3+WcaHJ/UZi8qHKnbdtug6TckEzcx4Tk+xVMOuzJ1DKFsmY/3v5gZLX/v/fPUkeKCV7AsUZU4FCchYWPCBHStehOnG/EsxvzNpn0N3/ygEiJbcIw2v//7/v7nYUKOq+hkWKugAABiF8Xw=="], {{0, 28}, {28, 0}}, { 0, 255},ColorFunction->GrayLevel],BoxForm`ImageTag[ "Byte", ColorSpace ->. Values: 0 to 3 - default is 0 -furmark-fur-img-id number set the image for the fur color. Values: 0 to 1 - default is 0 -furmark-dxt5 use compressed textures (DXT5) in FurMark stress test Values: 0 to 3 - default is 0 -furmark-fur-img-id number set the image for the fur color. Values: 0 to 1 - default is 0 -furmark-dxt5 use compressed textures (DXT5) in FurMark stress test

1 0 1 1 0 1 1 0 0 1 1 0 1 1 1 1 - University of Toronto

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Dutching on 0 – 0, 0 – 1 and 0 - MarketFeeder

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Dutching on 0 0, 0 1 and 0 - MarketFeeder

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