print matrices
This commit is contained in:
Adam Wojdyla
parent
551db85da0
commit
5ba553d36b
156
jupyter.ipynb
156
jupyter.ipynb
@ -74,7 +74,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "e28202b168c9439a87a8e3a8369510bf",
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"model_id": "62e9cd302c2a459bba91ee276347ef20",
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"version_major": 2,
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"execution_count": 50,
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@ -290,7 +290,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"execution_count": 29,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -306,9 +306,6 @@
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" reconstructed matrix reconst_matrix, array of singular values s\n",
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" \"\"\"\n",
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" U,s,V = svd(image,full_matrices=False)\n",
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" print(f\"U MATRIX: {U}\")\n",
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" print(f\"S MATRIX: {s}\")\n",
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" print(f\"V MATRIX: {V}\")\n",
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" reconst_matrix = np.dot(U[:,:k],np.dot(np.diag(s[:k]),V[:k,:]))\n",
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"\n",
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" return reconst_matrix,s"
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"execution_count": 30,
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"metadata": {
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"pycharm": {
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@ -369,7 +366,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 31,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -400,7 +397,34 @@
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"execution_count": 32,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [],
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"source": [
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"# Print matrices\n",
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"def print_matrices(img_name, k):\n",
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" image=gray_images[img_name]\n",
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" original_shape = image.shape\n",
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" print(f\"Input image dimensions. Width:{original_shape[1]} Height:{original_shape[0]}\")\n",
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"\n",
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" U,s,V = svd(image,full_matrices=False)\n",
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" print(f\"Shape of U matrix: {U[:,:k].shape}\")\n",
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" print(f\"U MATRIX: {U[:,:k]}\")\n",
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" print('*' * 100)\n",
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" print(f\"Shape of S matrix: {s[:k].shape}\")\n",
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" print(f\"S MATRIX: {np.diag(s[:k])}\")\n",
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" print('*' * 100)\n",
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" print(f\"Shape of V matrix: {V[:k,:].shape}\")\n",
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" print(f\"V MATRIX: {V[:k,:]}\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -410,7 +434,45 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "2062e134cdf2460191f6c2c6b5ceb1f3",
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"model_id": "69f9d84be21d4b09b9b7687b9ac5277c",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'camera', 'coin', 'clock', 'bl…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<function __main__.print_matrices(img_name, k)>"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"interact(print_matrices, img_name=list_widget, k=int_slider_widget)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "962ee74402d742d2aa9fac156d95ee88",
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"version_major": 2,
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"version_minor": 0
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},
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@ -439,7 +501,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"execution_count": 35,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -491,7 +553,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"execution_count": 36,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -526,7 +588,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 57,
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"execution_count": 37,
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"metadata": {
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"name": "#%%\n"
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},
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{
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"cell_type": "code",
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"execution_count": 58,
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"execution_count": 38,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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{
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"data": {
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"model_id": "cbd795b4af504c9998a5d9f0b41c0d47",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"interactive(children=(Dropdown(description='img_name', options=('cat', 'astro', 'coffee'), value='cat'), IntSl…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<function __main__.print_matrices(img_name, k)>"
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]
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},
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"execution_count": 38,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"interact(print_matrices, img_name=list_widget, k=int_slider_widget)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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}
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "42e07c36e033494a8c242ed336d6f2d8",
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"version_major": 2,
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"version_minor": 0
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},
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@ -603,7 +703,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"execution_count": 40,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -618,14 +718,14 @@
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" image = color_images[img_name]\n",
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" original_shape = image.shape\n",
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" image_reconst_layers = [compress_svd(image[:,:,i],k)[0] for i in range(3)]\n",
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" print(image_reconst_layers)\n",
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" image_reconst = np.zeros(image.shape)\n",
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" for i in range(3):\n",
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" image_reconst[:,:,i] = image_reconst_layers[i]\n",
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" \n",
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" compression_ratio =100.0*3* (k*(original_shape[0] + original_shape[1])+k)/(original_shape[0]*original_shape[1]*original_shape[2])\n",
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" plt.title(\"compression ratio={:.2f}\".format(compression_ratio)+\"%\")\n",
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" \n",
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" plt.imshow(image_reconst)"
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" plt.imshow(image_reconst, vmin=0, vmax=255)\n"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 60,
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"execution_count": 41,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"execution_count": 42,
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"metadata": {
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"pycharm": {
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"name": "#%%\n"
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@ -675,7 +775,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "a76ffdc4780549c8b0e5fef4db6d4f01",
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"version_major": 2,
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"version_minor": 0
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},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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@ -707,7 +806,6 @@
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"pycharm": {
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"name": "#%%\n"
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}
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