cudaregress 1.0.0.002

data analysis/cudaregress.py

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+# Titan Robotics Team 2022: CUDA-based Regressions Module
+# Written by Arthur Lu & Jacob Levine
+# Notes:
+#   this should be imported as a python module using 'import cudaregress'
+#   this should be included in the local directory or environment variable
+#   this module is cuda-optimized and vectorized (except for one small part)
+# setup:
+
+__version__ = "1.0.0.002"
+
+# changelog should be viewed using print(cudaregress.__changelog__)
+__changelog__ = """
+1.0.0.002:
+    -Added more parameters to log, exponential, polynomial
+    -
+
+1.0.0.001:
+    -initial release, with linear, log, exponential, polynomial, and sigmoid kernels
+    -already vectorized (except for polynomial generation) and CUDA-optimized
+
+"""
+
+__author__ = (
+    "Jacob Levine <jlevine@imsa.edu>",
+)
+
+__all__ = [
+    'factorial',
+    'take_all_pwrs',
+    'num_poly_terms',
+    'set_device',
+    'LinearRegKernel',
+    'SigmoidalRegKernel',
+    'LogRegKernel',
+    'PolyRegKernel',
+    'ExpRegKernel',
+    'SigmoidalRegKernelArthur',
+    'SGDTrain',
+    'CustomTrain'
+]
+
+
+# imports (just one for now):
+
+import torch
+
+device = "cuda:0" if torch.torch.cuda.is_available() else "cpu"
+
+#todo: document completely
+
+def factorial(n):
+    if n==0:
+        return 1
+    else:
+        return n*factorial(n-1)
+def num_poly_terms(num_vars, power):
+    if power == 0:
+        return 0
+    return int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1)) + nt(num_vars, power-1)
+
+def take_all_pwrs(vec,pwr):
+    #todo: vectorize (kinda)
+    combins=torch.combinations(vec, r=pwr, with_replacement=True)
+    out=torch.ones(combins.size()[0])
+    for i in torch.t(combins):
+        out *= i
+    return torch.cat(out,take_all_pwrs(vec, pwr-1))
+
+def set_device(new_device):
+    global device
+    device=new_device
+
+class LinearRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def forward(self,mtx):
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return torch.matmul(self.weights,mtx)+long_bias
+
+class SigmoidalRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    sigmoid=torch.nn.Sigmoid()
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def forward(self,mtx):
+        long_bias=self.bias.repeat([1,mtx.size()[1]])
+        return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)
+
+class SigmoidalRegKernelArthur():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    sigmoid=torch.nn.Sigmoid()
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias==torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (scal_mult*self.sigmoid(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class LogRegKernel():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias==torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (scal_mult*torch.log(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class ExpRegKernel():
+    parameters= []
+    weights=None
+    in_bias=None
+    scal_mult=None
+    out_bias=None
+    def __init__(self, num_vars):
+        self.weights=torch.rand(num_vars, requires_grad=True, device=device)
+        self.in_bias=torch.rand(1, requires_grad=True, device=device)
+        self.scal_mult=torch.rand(1, requires_grad=True, device=device)
+        self.out_bias==torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.in_bias, self.scal_mult, self.out_bias]
+    def forward(self,mtx):
+        long_in_bias=self.in_bias.repeat([1,mtx.size()[1]])
+        long_out_bias=self.out_bias.repeat([1,mtx.size()[1]])
+        return (scal_mult*torch.exp(torch.matmul(self.weights,mtx)+long_in_bias))+long_out_bias
+
+class PolyRegKernel():
+    parameters= []
+    weights=None
+    bias=None
+    power=None
+    def __init__(self, num_vars, power):
+        self.power=power
+        num_terms=num_poly_terms(num_vars, power)
+        self.weights=torch.rand(num_terms, requires_grad=True, device=device)
+        self.bias=torch.rand(1, requires_grad=True, device=device)
+        self.parameters=[self.weights,self.bias]
+    def forward(self,mtx):
+        #TODO: Vectorize the last part
+        cols=[]
+        for i in torch.t(mtx):
+            cols.append(take_all_pwrs(i,self.power))
+        new_mtx=torch.t(torch.stack(cols))
+        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):
+    optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)
+    data_cuda=data.to(device)
+    ground_cuda=ground.to(device)
+    if (return_losses):
+        losses=[]
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                losses.append(ls.item())
+                ls.backward()
+                optim.step()
+        return [kernel,losses]
+    else:
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                ls.backward()
+                optim.step()
+        return kernel
+
+def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):
+    data_cuda=data.to(device)
+    ground_cuda=ground.to(device)
+    if (return_losses):
+        losses=[]
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data)
+                ls=loss(pred,ground)
+                losses.append(ls.item())
+                ls.backward()
+                optim.step()
+        return [kernel,losses]
+    else:
+        for i in range(iterations):
+            with torch.set_grad_enabled(True):
+                optim.zero_grad()
+                pred=kernel.forward(data_cuda)
+                ls=loss(pred,ground_cuda)
+                ls.backward()
+                optim.step()
+        return kernel