From fa97eb9071ad4333b3415307e0d49d192d274b98 Mon Sep 17 00:00:00 2001
From: Saad Jbabdi <saad@SJMBPr2016.local>
Date: Thu, 5 Mar 2020 14:05:31 +0000
Subject: [PATCH] added rbf

---
 .../fit_model-checkpoint.ipynb                | 157 ++++++++++++
 .../play_rbf-checkpoint.ipynb                 |  97 ++++++++
 talks/matlab_vs_python/rbf/fit_model.ipynb    | 232 ++++++++++++++++++
 talks/matlab_vs_python/rbf/play_rbf.ipynb     |  97 ++++++++
 4 files changed, 583 insertions(+)
 create mode 100644 talks/matlab_vs_python/rbf/.ipynb_checkpoints/fit_model-checkpoint.ipynb
 create mode 100644 talks/matlab_vs_python/rbf/.ipynb_checkpoints/play_rbf-checkpoint.ipynb
 create mode 100644 talks/matlab_vs_python/rbf/fit_model.ipynb
 create mode 100644 talks/matlab_vs_python/rbf/play_rbf.ipynb

diff --git a/talks/matlab_vs_python/rbf/.ipynb_checkpoints/fit_model-checkpoint.ipynb b/talks/matlab_vs_python/rbf/.ipynb_checkpoints/fit_model-checkpoint.ipynb
new file mode 100644
index 0000000..1e34a51
--- /dev/null
+++ b/talks/matlab_vs_python/rbf/.ipynb_checkpoints/fit_model-checkpoint.ipynb
@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x121ca4278>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit a model to some data\n",
+    "# Model is:\n",
+    "#    prediction = M0 * exp(-TE/T2)*(1-exp(-TR/T1))\n",
+    "#    where M0,T1,T2 are unknown parameters and TE/TR are experimental parameters\n",
+    "\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import minimize\n",
+    "\n",
+    "\n",
+    "TEs = np.array([10,40,60,80]) # TE values in ms\n",
+    "TRs = np.array([.5,1,1.5,2])  # TR in seconds\n",
+    "\n",
+    "# All combinations of TEs/TRs\n",
+    "combinations = np.array([(x,y) for x in TEs for y in TRs])\n",
+    "TEs,TRs = combinations[:,0],combinations[:,1]\n",
+    "\n",
+    "# function for our model\n",
+    "def forward(p):\n",
+    "    M0,T1,T2 = p\n",
+    "    return M0*np.exp(-TEs/T2)*(1-np.exp(-TRs/T1))\n",
+    "\n",
+    "# simulate data using model \n",
+    "true_p = [100,.8,50]\n",
+    "data   = forward(true_p)\n",
+    "data   = data + np.random.randn(data.size)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now for the fitting\n",
+    "# we need a cost function:\n",
+    "\n",
+    "def cf(p):\n",
+    "    pred = forward(p)\n",
+    "    return np.mean((pred-data)**2)/2\n",
+    " \n",
+    "# always a good idea to calculate gradient\n",
+    "def forward_deriv(p):\n",
+    "    M0,T1,T2 = p\n",
+    "    E1,E2 = np.exp(-TEs/T2),np.exp(-TRs/T1)\n",
+    "    \n",
+    "    dfdM0 = E2*(1-E1)\n",
+    "    dfdT1 = M0*E2*(-E1/T1**2)\n",
+    "    dfdT2 = M0*(E2/T2**2)*(1-E1)\n",
+    "    return np.array([dfdM0,dfdT1,dfdT2])\n",
+    "    \n",
+    "def gradient(p):\n",
+    "    pred  = forward(p)\n",
+    "    deriv = forward_deriv(p)\n",
+    "    return np.mean( deriv * (pred-data)[None,:],axis=1)\n",
+    "\n",
+    "# get ready to minimize\n",
+    "p0 = [200,70,1000] # some random guess\n",
+    "method = 'TNC'\n",
+    "\n",
+    "arguments = {'x0':p0,'method':method,'jac':gradient}\n",
+    "\n",
+    "result = minimize(cf,**arguments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x121f34eb8>]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data)\n",
+    "plt.plot(forward(result.x))"
+   ]
+  },
+  {
+   "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/talks/matlab_vs_python/rbf/.ipynb_checkpoints/play_rbf-checkpoint.ipynb b/talks/matlab_vs_python/rbf/.ipynb_checkpoints/play_rbf-checkpoint.ipynb
new file mode 100644
index 0000000..d431889
--- /dev/null
+++ b/talks/matlab_vs_python/rbf/.ipynb_checkpoints/play_rbf-checkpoint.ipynb
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#!/usr/env/python\n",
+    "\n",
+    "# Python version of RBF fitting\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Generate noisy sine wave\n",
+    "x = np.linspace(0,10,100)\n",
+    "y = np.sin(3*x) + np.random.randn(x.size)*.5\n",
+    " \n",
+    "# Define RBF atom\n",
+    "# two options:\n",
+    "#   inline function definition (not available in matlab)\n",
+    "#   lambda : like @ in matlab\n",
+    "\n",
+    "sig = 2\n",
+    "# option 1\n",
+    "# def rbf(x,c):\n",
+    "#     return np.exp(-(x-c)**2/sig**2)\n",
+    "# option 2\n",
+    "rbf = lambda x,c : np.exp(-(x-c)**2/sig**2)\n",
+    "\n",
+    "# create design matrix\n",
+    "# (use list comprehension to show off)\n",
+    "xi     = np.linspace(0,10,15)\n",
+    "desmat = [rbf(x,c) for c in xi] \n",
+    "desmat = np.asarray(desmat).T\n",
+    "\n",
+    "# invert model\n",
+    "beta   = np.linalg.pinv(desmat)@y.T\n",
+    "\n",
+    "# plot fit\n",
+    "plt.figure()\n",
+    "plt.plot(x,y,'.')\n",
+    "plt.plot(x,desmat,'k') \n",
+    "plt.plot(x,desmat@beta)\n",
+    "\n",
+    "\n",
+    "# make it pretty\n",
+    "plt.grid()\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('RBF fitting')\n",
+    "plt.savefig('/Users/saad/Desktop/RBF.pdf')\n",
+    "\n"
+   ]
+  },
+  {
+   "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/talks/matlab_vs_python/rbf/fit_model.ipynb b/talks/matlab_vs_python/rbf/fit_model.ipynb
new file mode 100644
index 0000000..40754f9
--- /dev/null
+++ b/talks/matlab_vs_python/rbf/fit_model.ipynb
@@ -0,0 +1,232 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x121ca4278>]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Fit a model to some data\n",
+    "# Model is:\n",
+    "#    prediction = M0 * exp(-TE/T2)*(1-exp(-TR/T1))\n",
+    "#    where M0,T1,T2 are unknown parameters and TE/TR are experimental parameters\n",
+    "\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.optimize import minimize\n",
+    "\n",
+    "\n",
+    "TEs = np.array([10,40,60,80]) # TE values in ms\n",
+    "TRs = np.array([.5,1,1.5,2])  # TR in seconds\n",
+    "\n",
+    "# All combinations of TEs/TRs\n",
+    "combinations = np.array([(x,y) for x in TEs for y in TRs])\n",
+    "TEs,TRs = combinations[:,0],combinations[:,1]\n",
+    "\n",
+    "# function for our model\n",
+    "def forward(p):\n",
+    "    M0,T1,T2 = p\n",
+    "    return M0*np.exp(-TEs/T2)*(1-np.exp(-TRs/T1))\n",
+    "\n",
+    "# simulate data using model \n",
+    "true_p = [100,.8,50]\n",
+    "data   = forward(true_p)\n",
+    "data   = data + np.random.randn(data.size)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/scipy/optimize/_minimize.py:506: RuntimeWarning: Method Nelder-Mead does not use gradient information (jac).\n",
+      "  RuntimeWarning)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now for the fitting\n",
+    "# we need a cost function:\n",
+    "\n",
+    "def cf(p):\n",
+    "    pred = forward(p)\n",
+    "    return np.mean((pred-data)**2)/2\n",
+    " \n",
+    "# always a good idea to calculate gradient\n",
+    "def forward_deriv(p):\n",
+    "    M0,T1,T2 = p\n",
+    "    E1,E2    = np.exp(-TEs/T2),np.exp(-TRs/T1)\n",
+    "    \n",
+    "    dfdM0 = E2*(1-E1)\n",
+    "    dfdT1 = M0*E2*(-E1/T1**2)\n",
+    "    dfdT2 = M0*(E2/T2**2)*(1-E1)\n",
+    "    return np.array([dfdM0,dfdT1,dfdT2])\n",
+    "\n",
+    "def forward_deriv2(p):\n",
+    "    M0,T1,T2 = p\n",
+    "    E1,E2    = np.exp(-TEs/T2),np.exp(-TRs/T1)\n",
+    "    \n",
+    "    dfdM0dM0 = 0\n",
+    "    dfdM0dT1 = -E2\n",
+    "    dfdM0dT2 = (1-E1)\n",
+    "\n",
+    "    dfdT1dM0 = E2*(-E1/T1**2)\n",
+    "    dfdT1dT1 = M0*E2*(-E1/T1**4)\n",
+    "    dfdT1dT2 = M0*(E2/T2**4)*(1-E1)\n",
+    " \n",
+    "    dfdT2dM0 = (E2/T2**2)*(1-E1)\n",
+    "    dfdT2dT1 = M0*(E2/T2**2)*(1-E1/T1**2)\n",
+    "    dfdT2dT2 = M0*(E2/T2**4)*(1-E1)\n",
+    "\n",
+    "    return np.array([dfdM0dM0,dfdM0dT1,dfdM0dT2],[dfdT1dM0,dfdT1dT1,dfdT1dT2],[dfdT2dM0,dfdT2dT1,dfdT2dT2])\n",
+    "\n",
+    "def gradient(p):\n",
+    "    pred  = forward(p)\n",
+    "    deriv = forward_deriv(p)\n",
+    "    return np.mean( deriv * (pred-data)[None,:],axis=1)\n",
+    "\n",
+    "def hess(p):\n",
+    "    pred   = forward(p)\n",
+    "    deriv  = forward_deriv(p)\n",
+    "    d2Fdp2 = forward_deriv2(p)\n",
+    "    \n",
+    "    deriv*deriv\n",
+    "\n",
+    "\n",
+    "# get ready to minimize\n",
+    "p0 = [200,1,70] # some random guess\n",
+    "method = 'Nelder-Mead'\n",
+    "\n",
+    "arguments = {'x0':p0,'method':method,'jac':gradient}\n",
+    "\n",
+    "result = minimize(cf,**arguments)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x123074ef0>]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data)\n",
+    "plt.plot(forward(result.x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(forward_deriv(p0),axis=1).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 0.8, 50]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "true_p"
+   ]
+  },
+  {
+   "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/talks/matlab_vs_python/rbf/play_rbf.ipynb b/talks/matlab_vs_python/rbf/play_rbf.ipynb
new file mode 100644
index 0000000..d431889
--- /dev/null
+++ b/talks/matlab_vs_python/rbf/play_rbf.ipynb
@@ -0,0 +1,97 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#!/usr/env/python\n",
+    "\n",
+    "# Python version of RBF fitting\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Generate noisy sine wave\n",
+    "x = np.linspace(0,10,100)\n",
+    "y = np.sin(3*x) + np.random.randn(x.size)*.5\n",
+    " \n",
+    "# Define RBF atom\n",
+    "# two options:\n",
+    "#   inline function definition (not available in matlab)\n",
+    "#   lambda : like @ in matlab\n",
+    "\n",
+    "sig = 2\n",
+    "# option 1\n",
+    "# def rbf(x,c):\n",
+    "#     return np.exp(-(x-c)**2/sig**2)\n",
+    "# option 2\n",
+    "rbf = lambda x,c : np.exp(-(x-c)**2/sig**2)\n",
+    "\n",
+    "# create design matrix\n",
+    "# (use list comprehension to show off)\n",
+    "xi     = np.linspace(0,10,15)\n",
+    "desmat = [rbf(x,c) for c in xi] \n",
+    "desmat = np.asarray(desmat).T\n",
+    "\n",
+    "# invert model\n",
+    "beta   = np.linalg.pinv(desmat)@y.T\n",
+    "\n",
+    "# plot fit\n",
+    "plt.figure()\n",
+    "plt.plot(x,y,'.')\n",
+    "plt.plot(x,desmat,'k') \n",
+    "plt.plot(x,desmat@beta)\n",
+    "\n",
+    "\n",
+    "# make it pretty\n",
+    "plt.grid()\n",
+    "plt.xlabel('x')\n",
+    "plt.ylabel('y')\n",
+    "plt.title('RBF fitting')\n",
+    "plt.savefig('/Users/saad/Desktop/RBF.pdf')\n",
+    "\n"
+   ]
+  },
+  {
+   "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": "3.6.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
-- 
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