{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Gaussian filtering and smoothing (discrete)\n",
    "\n",
    "Provided is an example of discrete, linear state-space models on which one can perform Bayesian filtering and smoothing in order to obtain\n",
    "a posterior distribution over a latent state trajectory based on noisy observations.\n",
    "In order to understand the theory behind these methods in detail we refer to [1] and [2].\n",
    "\n",
    "\n",
    "**References**:\n",
    "> [1] Särkkä, Simo, and Solin, Arno. Applied Stochastic Differential Equations. Cambridge University Press, 2019.  \n",
    ">\n",
    "> [2] Särkkä, Simo. Bayesian Filtering and Smoothing. Cambridge University Press, 2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import probnum as pn\n",
    "from probnum import filtsmooth, randvars, randprocs\n",
    "from probnum.problems import TimeSeriesRegressionProblem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(seed=123)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_125474/236124620.py:5: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  set_matplotlib_formats(\"pdf\", \"svg\")\n"
     ]
    }
   ],
   "source": [
    "# Make inline plots vector graphics instead of raster graphics\n",
    "%matplotlib inline\n",
    "from IPython.display import set_matplotlib_formats\n",
    "\n",
    "set_matplotlib_formats(\"pdf\", \"svg\")\n",
    "\n",
    "# Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "plt.style.use(\"../../probnum.mplstyle\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Linear Discrete** State-Space Model: Car Tracking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We showcase the arguably most simple case in which we consider the following state-space model. Consider matrices $A \\in \\mathbb{R}^{d \\times d}$ and $H \\in \\mathbb{R}^{m \\times d}$ where $d$ is the state dimension and $m$ is the dimension of the measurements. Then we define the dynamics and the measurement model as follows:\n",
    "\n",
    "For $k = 1, \\dots, K$ and $x_0 \\sim \\mathcal{N}(\\mu_0, \\Sigma_0)$:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "    \\boldsymbol{x}_k &\\sim \\mathcal{N}(\\boldsymbol{A} \\, \\boldsymbol{x}_{k-1}, \\boldsymbol{Q}) \\\\\n",
    "    \\boldsymbol{y}_k &\\sim \\mathcal{N}(\\boldsymbol{H} \\, \\boldsymbol{x}_k, \\boldsymbol{R})\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "This defines a dynamics model that assumes a state $\\boldsymbol{x}_k$ in a **discrete** sequence of states arising from a linear projection of the previous state $x_{k-1}$ corrupted with additive Gaussian noise under a **process noise** covariance matrix $Q$.  \n",
    "Similarly, the measurements $\\boldsymbol{y}_k$ are assumed to be linear projections of the latent state under additive Gaussian noise according to a **measurement noise** covariance $R$.\n",
    "In the following example we consider projections and covariances that are constant over the state and measurement trajectories (linear time invariant, or **LTI**). Note that this can be generalized to a linear time-varying state-space model, as well. Then $A$ is a function $A: \\mathbb{T} \\rightarrow \\mathbb{R}^{d \\times d}$ and $H$ is a function $H: \\mathbb{T} \\rightarrow \\mathbb{R}^{m \\times d}$ where $\\mathbb{T}$ is the \"time dimension\".\n",
    "\n",
    "In other words, here, every relationship is linear and every distribution is a Gaussian distribution.\n",
    "Under these simplifying assumptions it is possible to obtain a filtering posterior distribution over the state trajectory $(\\boldsymbol{x}_k)_{k=1}^{K}$ by using a \n",
    "**Kalman Filter**. The example is taken from Example 3.6 in [2]."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define State-Space Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I. Discrete Dynamics Model: Linear, Time-Invariant, Gaussian Transitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_dim = 4\n",
    "observation_dim = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_t = 0.2\n",
    "\n",
    "# Define linear transition operator\n",
    "dynamics_transition_matrix = np.eye(state_dim) + delta_t * np.diag(np.ones(2), 2)\n",
    "\n",
    "# Define process noise (covariance) matrix\n",
    "noise_matrix = (\n",
    "    np.diag(np.array([delta_t ** 3 / 3, delta_t ** 3 / 3, delta_t, delta_t]))\n",
    "    + np.diag(np.array([delta_t ** 2 / 2, delta_t ** 2 / 2]), 2)\n",
    "    + np.diag(np.array([delta_t ** 2 / 2, delta_t ** 2 / 2]), -2)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create a discrete, LTI Gaussian dynamics model, `probnum` provides the `LTIGaussian` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create discrete, Linear Time-Invariant Gaussian dynamics model\n",
    "noise = randvars.Normal(mean=np.zeros(state_dim), cov=noise_matrix)\n",
    "dynamics_model = randprocs.markov.discrete.LTIGaussian(\n",
    "    transition_matrix=dynamics_transition_matrix,\n",
    "    noise=noise,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II. Discrete Measurement Model: Linear, Time-Invariant, Gaussian Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "measurement_marginal_variance = 0.5\n",
    "measurement_matrix = np.eye(observation_dim, state_dim)\n",
    "measurement_noise_matrix = measurement_marginal_variance * np.eye(observation_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise = randvars.Normal(mean=np.zeros(observation_dim), cov=measurement_noise_matrix)\n",
    "measurement_model = randprocs.markov.discrete.LTIGaussian(\n",
    "    transition_matrix=measurement_matrix,\n",
    "    noise=noise,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### III. Initial State Random Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu_0 = np.zeros(state_dim)\n",
    "sigma_0 = 0.5 * measurement_marginal_variance * np.eye(state_dim)\n",
    "initial_state_rv = randvars.Normal(mean=mu_0, cov=sigma_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "prior_process = randprocs.markov.MarkovSequence(\n",
    "    transition=dynamics_model, initrv=initial_state_rv, initarg=0.0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate Data for the State-Space Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, sample both latent states and noisy observations from the specified state-space model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_grid = np.arange(0.0, 10.0, step=delta_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_states, observations = randprocs.markov.utils.generate_artificial_measurements(\n",
    "    rng=rng,\n",
    "    prior_process=prior_process,\n",
    "    measmod=measurement_model,\n",
    "    times=time_grid,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "regression_problem = TimeSeriesRegressionProblem(\n",
    "    observations=observations,\n",
    "    locations=time_grid,\n",
    "    measurement_models=[measurement_model] * len(time_grid),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kalman Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I. Kalman Filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "kalman_filter = filtsmooth.gaussian.Kalman(prior_process)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### II. Perform Kalman Filtering + Rauch-Tung-Striebel Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_posterior, _ = kalman_filter.filtsmooth(regression_problem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `filtsmooth` returns a `KalmanPosterior` object which provides convenience functions for e.g. sampling and interpolation.\n",
    "We can also extract the just computed posterior smoothing state variables.\n",
    "This yields a list of Gaussian random variables from which we can extract the statistics in order to visualize them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = state_posterior.locations\n",
    "posterior_state_rvs = (\n",
    "    state_posterior.states\n",
    ")  # List of <num_time_points> Normal Random Variables\n",
    "posterior_state_means = posterior_state_rvs.mean  # Shape: (num_time_points, state_dim)\n",
    "posterior_state_covs = (\n",
    "    posterior_state_rvs.cov\n",
    ")  # Shape: (num_time_points, state_dim, state_dim)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/svg+xml": [
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       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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       " <metadata>\n",
       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
       "   <cc:Work>\n",
       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
       "    <dc:date>2022-02-09T12:35:00.229644</dc:date>\n",
       "    <dc:format>image/svg+xml</dc:format>\n",
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       " </g>\n",
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       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 576x324 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "state_fig = plt.figure()\n",
    "state_fig_gs = gridspec.GridSpec(ncols=2, nrows=2, figure=state_fig)\n",
    "\n",
    "ax_00 = state_fig.add_subplot(state_fig_gs[0, 0])\n",
    "ax_01 = state_fig.add_subplot(state_fig_gs[0, 1])\n",
    "ax_10 = state_fig.add_subplot(state_fig_gs[1, 0])\n",
    "ax_11 = state_fig.add_subplot(state_fig_gs[1, 1])\n",
    "\n",
    "# Plot means\n",
    "mu_x_1, mu_x_2, mu_x_3, mu_x_4 = [posterior_state_means[:, i] for i in range(state_dim)]\n",
    "\n",
    "ax_00.plot(grid, mu_x_1, label=\"posterior mean\")\n",
    "ax_01.plot(grid, mu_x_2)\n",
    "ax_10.plot(grid, mu_x_3)\n",
    "ax_11.plot(grid, mu_x_4)\n",
    "\n",
    "# Plot marginal standard deviations\n",
    "std_x_1, std_x_2, std_x_3, std_x_4 = [\n",
    "    np.sqrt(posterior_state_covs[:, i, i]) for i in range(state_dim)\n",
    "]\n",
    "\n",
    "ax_00.fill_between(\n",
    "    grid,\n",
    "    mu_x_1 - 1.96 * std_x_1,\n",
    "    mu_x_1 + 1.96 * std_x_1,\n",
    "    alpha=0.2,\n",
    "    label=\"1.96 marginal stddev\",\n",
    ")\n",
    "ax_01.fill_between(grid, mu_x_2 - 1.96 * std_x_2, mu_x_2 + 1.96 * std_x_2, alpha=0.2)\n",
    "ax_10.fill_between(grid, mu_x_3 - 1.96 * std_x_3, mu_x_3 + 1.96 * std_x_3, alpha=0.2)\n",
    "ax_11.fill_between(grid, mu_x_4 - 1.96 * std_x_4, mu_x_4 + 1.96 * std_x_4, alpha=0.2)\n",
    "\n",
    "# Plot groundtruth\n",
    "obs_x_1, obs_x_2 = [observations[:, i] for i in range(observation_dim)]\n",
    "\n",
    "ax_00.scatter(time_grid, obs_x_1, marker=\".\", label=\"measurements\")\n",
    "ax_01.scatter(time_grid, obs_x_2, marker=\".\")\n",
    "\n",
    "# Add labels etc.\n",
    "ax_00.set_xlabel(\"t\")\n",
    "ax_01.set_xlabel(\"t\")\n",
    "ax_10.set_xlabel(\"t\")\n",
    "ax_11.set_xlabel(\"t\")\n",
    "\n",
    "ax_00.set_title(r\"$x_1$\")\n",
    "ax_01.set_title(r\"$x_2$\")\n",
    "ax_10.set_title(r\"$x_3$\")\n",
    "ax_11.set_title(r\"$x_4$\")\n",
    "handles, labels = ax_00.get_legend_handles_labels()\n",
    "state_fig.legend(handles, labels, loc=\"center left\", bbox_to_anchor=(1, 0.5))\n",
    "\n",
    "state_fig.tight_layout()"
   ]
  }
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