diff --git a/examples/keypoint_slds.ipynb b/examples/keypoint_slds.ipynb index a4d2386..15a2322 100644 --- a/examples/keypoint_slds.ipynb +++ b/examples/keypoint_slds.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "varying-morrison", "metadata": {}, "outputs": [], @@ -29,20 +29,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "expressed-christian", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-10-08 16:24:18.130080: W external/org_tensorflow/tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", - "WARNING:absl:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)\n", - "2022-10-08 16:24:18.130112: W external/org_tensorflow/tensorflow/stream_executor/cuda/cuda_driver.cc:269] failed call to cuInit: UNKNOWN ERROR (303)\n" - ] - } - ], + "outputs": [], "source": [ "# load dictionary {session_name: ndarray (time,keypoints,2)}\n", "keypoint_data_dict = pickle.load(open('example_keypoint_coords.p','rb'))\n", @@ -54,17 +44,6 @@ "Y,mask = jnp.array(Y),jnp.array(mask)" ] }, - { - "cell_type": "code", - "execution_count": 5, - "id": "convenient-wednesday", - "metadata": {}, - "outputs": [], - "source": [ - "Y = Y[:2]\n", - "mask = mask[:2]" - ] - }, { "cell_type": "markdown", "id": "packed-shade", @@ -75,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "contrary-future", "metadata": {}, "outputs": [], @@ -127,7 +106,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "falling-overall", "metadata": {}, "outputs": [], @@ -151,23 +130,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "posted-lingerie", + "execution_count": null, + "id": "living-channels", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(np.arange(latent_dim)+1,np.cumsum(pca_model.explained_variance_ratio_))\n", "plt.xlabel('PCs')\n", @@ -189,60 +155,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "capable-grove", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "260319e619e84d6e872563d085e5d153", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/500 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_23841/404238302.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_iters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'betas'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pi'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresample_hdp_transitions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mstates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mtrans_hypparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ab'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Q'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mresample_ar_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mstates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mar_hypparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'z'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresample_stateseqs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mstates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "num_iters = 500\n", "plot_iters = 10\n", @@ -277,8 +193,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "challenging-century", + "execution_count": null, + "id": "embedded-melissa", "metadata": {}, "outputs": [], "source": [ @@ -294,22 +210,7 @@ "execution_count": null, "id": "otherwise-nomination", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "261b3292da634bf182e7e61ee81d2473", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/500 [00:00