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regression v 1.0.0.003

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art 2020-01-04 10:19:31 -06:00
parent 031e45ac19
commit 0fbb958dd9
1 changed files with 6 additions and 3 deletions
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9
data analysis/analysis/analysis.py
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@ -619,10 +619,12 @@ class Regression:
# this module is cuda-optimized and vectorized (except for one small part) # this module is cuda-optimized and vectorized (except for one small part)
# setup: # setup:
__version__ = "1.0.0.002" __version__ = "1.0.0.003"
# changelog should be viewed using print(analysis.regression.__changelog__) # changelog should be viewed using print(analysis.regression.__changelog__)
__changelog__ = """ __changelog__ = """
1.0.0.003:
- bug fixes
1.0.0.002: 1.0.0.002:
-Added more parameters to log, exponential, polynomial -Added more parameters to log, exponential, polynomial
-Added SigmoidalRegKernelArthur, because Arthur apparently needs -Added SigmoidalRegKernelArthur, because Arthur apparently needs
@ -653,12 +655,13 @@ class Regression:
'CustomTrain' 'CustomTrain'
] ]
global device
device = "cuda:0" if torch.torch.cuda.is_available() else "cpu" device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
#todo: document completely #todo: document completely
def set_device(self, new_device): def set_device(self, new_device):
global device
device=new_device device=new_device
class LinearRegKernel(): class LinearRegKernel():
@ -777,7 +780,7 @@ class Regression:
long_bias=self.bias.repeat([1,mtx.size()[1]]) long_bias=self.bias.repeat([1,mtx.size()[1]])
return torch.matmul(self.weights,new_mtx)+long_bias 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) optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
data_cuda=data.to(device) data_cuda=data.to(device)
ground_cuda=ground.to(device) ground_cuda=ground.to(device)