From 0fbb958dd9b42acddc143a86972c3e097f65fb5a Mon Sep 17 00:00:00 2001 From: art Date: Sat, 4 Jan 2020 10:19:31 -0600 Subject: [PATCH] regression v 1.0.0.003 --- data analysis/analysis/analysis.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/data analysis/analysis/analysis.py b/data analysis/analysis/analysis.py index 23e18282..14f8c9b4 100644 --- a/data analysis/analysis/analysis.py +++ b/data analysis/analysis/analysis.py @@ -619,10 +619,12 @@ class Regression: # this module is cuda-optimized and vectorized (except for one small part) # setup: - __version__ = "1.0.0.002" + __version__ = "1.0.0.003" # changelog should be viewed using print(analysis.regression.__changelog__) __changelog__ = """ + 1.0.0.003: + - bug fixes 1.0.0.002: -Added more parameters to log, exponential, polynomial -Added SigmoidalRegKernelArthur, because Arthur apparently needs @@ -653,12 +655,13 @@ class Regression: 'CustomTrain' ] + global device + device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" #todo: document completely def set_device(self, new_device): - global device device=new_device class LinearRegKernel(): @@ -777,7 +780,7 @@ class Regression: long_bias=self.bias.repeat([1,mtx.size()[1]]) return torch.matmul(self.weights,new_mtx)+long_bias - def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False): + def SGDTrain(self, kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False): optim=torch.optim.SGD(kernel.parameters, lr=learning_rate) data_cuda=data.to(device) ground_cuda=ground.to(device)