diff --git a/data analysis/cudaRegressTesting.ipynb b/data analysis/cudaRegressTesting.ipynb
deleted file mode 100644
index a684148f..00000000
--- a/data analysis/cudaRegressTesting.ipynb	
+++ /dev/null
@@ -1,248 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "device='cuda:0' if torch.cuda.is_available() else 'cpu'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def factorial(n):\n",
-    "    if n==0:\n",
-    "        return 1\n",
-    "    else:\n",
-    "        return n*factorial(n-1)\n",
-    "\n",
-    "def take_all_pwrs(vec,pwr):\n",
-    "    #todo: vectorize (kinda)\n",
-    "    combins=torch.combinations(vec, r=pwr, with_replacement=True)\n",
-    "    out=torch.ones(combins.size()[0])\n",
-    "    for i in torch.t(combins):\n",
-    "        out *= i\n",
-    "    return out\n",
-    "\n",
-    "def set_device(new_device):\n",
-    "    device=new_device\n",
-    "\n",
-    "class LinearRegKernel():\n",
-    "    parameters= []\n",
-    "    weights=None\n",
-    "    bias=None\n",
-    "    def __init__(self, num_vars):\n",
-    "        self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
-    "        self.bias=torch.rand(1, requires_grad=True, device=device)\n",
-    "        self.parameters=[self.weights,self.bias]\n",
-    "    def forward(self,mtx):\n",
-    "        long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
-    "        return torch.matmul(self.weights,mtx)+long_bias\n",
-    "    \n",
-    "class SigmoidalRegKernel():\n",
-    "    parameters= []\n",
-    "    weights=None\n",
-    "    bias=None\n",
-    "    sigmoid=torch.nn.Sigmoid()\n",
-    "    def __init__(self, num_vars):\n",
-    "        self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
-    "        self.bias=torch.rand(1, requires_grad=True, device=device)\n",
-    "        self.parameters=[self.weights,self.bias]\n",
-    "    def forward(self,mtx):\n",
-    "        long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
-    "        return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)\n",
-    "\n",
-    "class LogRegKernel():\n",
-    "    parameters= []\n",
-    "    weights=None\n",
-    "    bias=None\n",
-    "    def __init__(self, num_vars):\n",
-    "        self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
-    "        self.bias=torch.rand(1, requires_grad=True, device=device)\n",
-    "        self.parameters=[self.weights,self.bias]\n",
-    "    def forward(self,mtx):\n",
-    "        long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
-    "        return torch.log(torch.matmul(self.weights,mtx)+long_bias)\n",
-    "\n",
-    "class ExpRegKernel():\n",
-    "    parameters= []\n",
-    "    weights=None\n",
-    "    bias=None\n",
-    "    def __init__(self, num_vars):\n",
-    "        self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
-    "        self.bias=torch.rand(1, requires_grad=True, device=device)\n",
-    "        self.parameters=[self.weights,self.bias]\n",
-    "    def forward(self,mtx):\n",
-    "        long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
-    "        return torch.exp(torch.matmul(self.weights,mtx)+long_bias)\n",
-    "\n",
-    "class PolyRegKernel():\n",
-    "    parameters= []\n",
-    "    weights=None\n",
-    "    bias=None\n",
-    "    power=None\n",
-    "    def __init__(self, num_vars, power):\n",
-    "        self.power=power\n",
-    "        num_terms=int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1))\n",
-    "        self.weights=torch.rand(num_terms, requires_grad=True, device=device)\n",
-    "        self.bias=torch.rand(1, requires_grad=True, device=device)\n",
-    "        self.parameters=[self.weights,self.bias]\n",
-    "    def forward(self,mtx):\n",
-    "        #TODO: Vectorize the last part\n",
-    "        cols=[]\n",
-    "        for i in torch.t(mtx):\n",
-    "            cols.append(take_all_pwrs(i,self.power))\n",
-    "        new_mtx=torch.t(torch.stack(cols))\n",
-    "        long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
-    "        return torch.matmul(self.weights,new_mtx)+long_bias\n",
-    "\n",
-    "def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):\n",
-    "    optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)\n",
-    "    data_cuda=data.to(device)\n",
-    "    ground_cuda=ground.to(device)\n",
-    "    if (return_losses):\n",
-    "        losses=[]\n",
-    "        for i in range(iterations):\n",
-    "            with torch.set_grad_enabled(True):\n",
-    "                optim.zero_grad()\n",
-    "                pred=kernel.forward(data_cuda)\n",
-    "                ls=loss(pred,ground_cuda)\n",
-    "                losses.append(ls.item())\n",
-    "                ls.backward()\n",
-    "                optim.step()\n",
-    "        return [kernel,losses]\n",
-    "    else:\n",
-    "        for i in range(iterations):\n",
-    "            with torch.set_grad_enabled(True):\n",
-    "                optim.zero_grad()\n",
-    "                pred=kernel.forward(data_cuda)\n",
-    "                ls=loss(pred,ground_cuda)\n",
-    "                ls.backward()\n",
-    "                optim.step() \n",
-    "        return kernel\n",
-    "\n",
-    "def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):\n",
-    "    data_cuda=data.to(device)\n",
-    "    ground_cuda=ground.to(device)\n",
-    "    if (return_losses):\n",
-    "        losses=[]\n",
-    "        for i in range(iterations):\n",
-    "            with torch.set_grad_enabled(True):\n",
-    "                optim.zero_grad()\n",
-    "                pred=kernel.forward(data)\n",
-    "                ls=loss(pred,ground)\n",
-    "                losses.append(ls.item())\n",
-    "                ls.backward()\n",
-    "                optim.step()\n",
-    "        return [kernel,losses]\n",
-    "    else:\n",
-    "        for i in range(iterations):\n",
-    "            with torch.set_grad_enabled(True):\n",
-    "                optim.zero_grad()\n",
-    "                pred=kernel.forward(data_cuda)\n",
-    "                ls=loss(pred,ground_cuda)\n",
-    "                ls.backward()\n",
-    "                optim.step() \n",
-    "        return kernel"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tensor([[1.0000, 2.0000]], device='cuda:0', grad_fn=<AddBackward0>)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model=SGDTrain(LinearRegKernel(3),torch.tensor([[1,2],[3,4],[5,6]]).to(torch.float).cuda(),torch.tensor([[1,2]]).to(torch.float).cuda(),iterations=10000, learning_rate=.01, return_losses=True)\n",
-    "model[0].forward(torch.tensor([[1,2],[3,4],[5,6]]).to(torch.float).cuda())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[tensor([0.2347, 0.4494, 0.3156], device='cuda:0', requires_grad=True),\n",
-       " tensor([0.9541], device='cuda:0', requires_grad=True)]"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "kernel=LinearRegKernel(3)\n",
-    "kernel.parameters\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}