diff --git a/NoisyCircle.ipynb b/NoisyCircle.ipynb new file mode 100644 index 0000000..e5e12c0 --- /dev/null +++ b/NoisyCircle.ipynb @@ -0,0 +1,358 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Topological analysis of a noisy circle\n", + "_Test application for the topological tools_" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "**Imports**" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [], + "source": [ + "import numpy as np\n", + "from matplotlib import pyplot as plt\n", + "from model import persistence, decoding\n", + "import pandas as pd\n", + "%matplotlib inline" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "**Parameters**" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "N = 20\n", + "R = 1\n", + "SIGMA = 0.1" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Generating the circle" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "(20, 2)" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phase = np.linspace(0, 2 * np.pi, N)\n", + "points = np.array([np.cos(phase), np.sin(phase)]).swapaxes(0, 1) * R\n", + "points.shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "noise = np.random.normal(scale=SIGMA, size=points.shape)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "points_no_noise = points\n", + "points += noise" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(points[:, 0], points[:, 1])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "
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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "persistence.persistence(points)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [], + "source": [ + "dataset = pd.DataFrame(points, columns=['x', 'y'])" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": "
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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Decoding... done\n" + ] + }, + { + "data": { + "text/plain": "(20, 1)" + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "param = decoding.cohomological_parameterization(dataset)\n", + "param.shape" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [ + { + "data": { + "text/plain": "Text(0, 0.5, 'Cohomological parameter')" + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(np.roll(np.arctan2(points[:, 0], points[:, 1]), 5), np.roll(param, 5))\n", + "plt.xlabel('Phase')\n", + "plt.ylabel('Cohomological parameter')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/model/decoding.py b/model/decoding.py index d4cc75e..66d9589 100644 --- a/model/decoding.py +++ b/model/decoding.py @@ -1,4 +1,4 @@ - # -*- coding: utf-8 -*- +# -*- coding: utf-8 -*- """ Use cohomology to decode datasets with circular parameters @@ -14,7 +14,7 @@ from tqdm import trange import ripser -from persistence import persistence +from .persistence import persistence EPSILON = 0.0000000000001 @@ -48,18 +48,19 @@ def shortest_cycle(graph, node2, node1): while (math.isnan(prev_nodes[node1])): distances_buffer = distances for j in range(N): - possible_path_lengths = distances_buffer + graph[:,j] + possible_path_lengths = distances_buffer + graph[:, j] if (np.min(possible_path_lengths) < distances[j]): prev_nodes[j] = np.argmin(possible_path_lengths) distances[j] = np.min(possible_path_lengths) prev_nodes = prev_nodes.astype(int) cycle = [node1] while (cycle[0] != node2): - cycle.insert(0,prev_nodes[cycle[0]]) - cycle.insert(0,node1) + cycle.insert(0, prev_nodes[cycle[0]]) + cycle.insert(0, node1) return cycle -def cohomological_parameterization(X ,cocycle_number=1, coeff=2,weighted=False): + +def cohomological_parameterization(X, cocycle_number=1, coeff=2, weighted=False): """ Compute an angular parametrization on the data set corresponding to a given 1-cycle @@ -89,67 +90,67 @@ def cohomological_parameterization(X ,cocycle_number=1, coeff=2,weighted=False): cocycles = result['cocycles'] D = result['dperm2all'] dgm1 = diagrams[1] - idx = np.argsort(dgm1[:, 1] - dgm1[:, 0])[-cocycle_number] + idx = np.argsort(dgm1[:, 1] - dgm1[:, 0])[-cocycle_number] cocycle = cocycles[1][idx] persistence(X, homdim=1, coeff=coeff, show_largest_homology=0, Nsubsamples=0, save_path=None, cycle=idx) - thresh = dgm1[idx, 1]-EPSILON - + thresh = dgm1[idx, 1] - EPSILON + # Compute connectivity N = X.shape[0] - connectivity = np.zeros([N,N]) + connectivity = np.zeros([N, N]) for i in range(N): for j in range(i): if D[i, j] <= thresh: - connectivity[i,j] = 1 - cocycle_array = np.zeros([N,N]) - + connectivity[i, j] = 1 + cocycle_array = np.zeros([N, N]) + # Lift cocycle for i in range(cocycle.shape[0]): - cocycle_array[cocycle[i,0],cocycle[i,1]] = ( - ((cocycle[i,2] + coeff/2) % coeff) - coeff/2 - ) - + cocycle_array[cocycle[i, 0], cocycle[i, 1]] = ( + ((cocycle[i, 2] + coeff / 2) % coeff) - coeff / 2 + ) + # Weights if (weighted): def real_cocycle(x): - real_cocycle =( - connectivity * (cocycle_array + np.subtract.outer(x, x)) - ) + real_cocycle = ( + connectivity * (cocycle_array + np.subtract.outer(x, x)) + ) return np.ravel(real_cocycle) - + # Compute graph x0 = np.zeros(N) res = least_squares(real_cocycle, x0) real_cocyle_array = res.fun - real_cocyle_array = real_cocyle_array.reshape(N,N) + real_cocyle_array = real_cocyle_array.reshape(N, N) real_cocyle_array = real_cocyle_array - np.transpose(real_cocyle_array) - graph = np.array(real_cocyle_array>0).astype(float) - graph[graph==0] = np.inf - graph = (D + EPSILON) * graph # Add epsilon to avoid NaNs - + graph = np.array(real_cocyle_array > 0).astype(float) + graph[graph == 0] = np.inf + graph = (D + EPSILON) * graph # Add epsilon to avoid NaNs + # Compute weights - cycle_counts = np.zeros([N,N]) + cycle_counts = np.zeros([N, N]) iterator = trange(0, N, position=0, leave=True) iterator.set_description("Computing weights for decoding") for i in iterator: for j in range(N): - if (graph[i,j] != np.inf): + if (graph[i, j] != np.inf): cycle = shortest_cycle(graph, j, i) - for k in range(len(cycle)-1): - cycle_counts[cycle[k], cycle[k+1]] += 1 - - weights = cycle_counts / (D + EPSILON)**2 + for k in range(len(cycle) - 1): + cycle_counts[cycle[k], cycle[k + 1]] += 1 + + weights = cycle_counts / (D + EPSILON) ** 2 weights = np.sqrt(weights) else: - weights = np.outer(np.ones(N),np.ones(N)) - + weights = np.outer(np.ones(N), np.ones(N)) + def real_cocycle(x): - real_cocycle =( - weights * connectivity * (cocycle_array + np.subtract.outer(x, x)) - ) + real_cocycle = ( + weights * connectivity * (cocycle_array + np.subtract.outer(x, x)) + ) return np.ravel(real_cocycle) - + # Smooth cocycle print("Decoding...", end=" ") x0 = np.zeros(N) @@ -157,7 +158,7 @@ def cohomological_parameterization(X ,cocycle_number=1, coeff=2,weighted=False): decoding = res.x decoding = np.mod(decoding, 1) print("done") - + decoding = pd.DataFrame(decoding, columns=["decoding"]) decoding = decoding.set_index(X.index) return decoding @@ -180,9 +181,9 @@ def remove_feature(X, decoding, shift=0, cut_amplitude=1.0): Amplitude of the cut """ cuts = np.zeros(X.shape) - decoding = decoding.to_numpy()[:,0] + decoding = decoding.to_numpy()[:, 0] for i in range(X.shape[1]): effective_amplitude = cut_amplitude * (np.max(X[i]) - np.min(X[i])) - cuts[:,i] = effective_amplitude * ((decoding - shift) % 1) + cuts[:, i] = effective_amplitude * ((decoding - shift) % 1) reduced_data = X + cuts - return reduced_data \ No newline at end of file + return reduced_data diff --git a/model/persistence.py b/model/persistence.py index cdd6694..5910581 100644 --- a/model/persistence.py +++ b/model/persistence.py @@ -16,7 +16,7 @@ import ripser from persim import plot_diagrams import gudhi -from decorators import multi_input +from .decorators import multi_input def hausdorff(data1, data2, homdim, coeff):