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Commit f067b78

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update numpy tutorial notebook
1 parent 23424ed commit f067b78

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-356
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3 files changed

+1085
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‎.ipynb_checkpoints/understanding-numpy-checkpoint.ipynb

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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"source": [
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"'1.15.3'"
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]
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},
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"execution_count": 53,
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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"Theta(-1.2), Theta(2.6)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"--------------------------\n",
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"--------------------------\n",
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"\n",
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"## **Advanced NumPy**"
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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": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"from IPython.display import Image\n",
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"from IPython.core.display import display, HTML"
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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": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<img src=\"https://i.stack.imgur.com/p2PGi.png\"/>"
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],
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 18,
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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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"axis_visual = \"https://i.stack.imgur.com/p2PGi.png\"\n",
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"Image(url=axis_visual)"
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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": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<img src=\"https://www.oreilly.com/library/view/elegant-scipy/9781491922927/assets/elsp_0105.png\"/>"
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],
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 19,
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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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"arr_visual = \"https://www.oreilly.com/library/view/elegant-scipy/9781491922927/assets/elsp_0105.png\"\n",
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"Image(url=arr_visual)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **Computing statistics across axes**"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[ 0, 1, 2, 3, 4, 5],\n",
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" [ 6, 7, 8, 9, 10, 11],\n",
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" [12, 13, 14, 15, 16, 17],\n",
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" [18, 19, 20, 21, 22, 23],\n",
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" [24, 25, 26, 27, 28, 29]])"
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]
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},
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"execution_count": 8,
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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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"arr = np.arange(5 * 6).reshape(5, 6)\n",
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"arr"
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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": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[12., 13., 14., 15., 16., 17.]])"
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]
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},
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"execution_count": 13,
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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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"arr.mean(axis=0, keepdims=True)"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"# what would be the result for:\n",
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"avg = arr.mean(axis=1, keepdims=True)\n",
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"\n",
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"# similarly, max, min, std, etc."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **Broadcasting**"
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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": 50,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<img src=\"https://jakevdp.github.io/PythonDataScienceHandbook/figures/02.05-broadcasting.png\" width=\"720\" height=\"480\"/>"
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],
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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"execution_count": 50,
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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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"bcast_visual = \"https://jakevdp.github.io/PythonDataScienceHandbook/figures/02.05-broadcasting.png\"\n",
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"Image(url=bcast_visual, width=720, height=480)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **RandomState**\n",
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"\n",
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"For reproducing the results, fix the seed:\n",
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"\n",
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" A fixed seed and a fixed series of calls to 'RandomState' methods using the same parameters will always produce the same results up to roundoff error except when the values were incorrect."
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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": 72,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[2, 3, 1, 3, 3, 0],\n",
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" [1, 1, 1, 3, 2, 1],\n",
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" [4, 3, 0, 2, 4, 4]])"
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]
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},
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"execution_count": 72,
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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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"rng = np.random.RandomState(seed=42)\n",
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"data = rng.randint(-1, 5, (3, 6))\n",
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"data"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### **Sampling from Distributions**"
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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": 75,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[[ 0.49671415, -0.1382643 ],\n",
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" [ 0.64768854, 1.52302986],\n",
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" [-0.23415337, -0.23413696],\n",
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" [ 1.57921282, 0.76743473]],\n",
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"\n",
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" [[-0.46947439, 0.54256004],\n",
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" [-0.46341769, -0.46572975],\n",
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" [ 0.24196227, -1.91328024],\n",
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" [-1.72491783, -0.56228753]],\n",
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"\n",
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" [[-1.01283112, 0.31424733],\n",
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" [-0.90802408, -1.4123037 ],\n",
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" [ 1.46564877, -0.2257763 ],\n",
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" [ 0.0675282 , -1.42474819]]])"
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]
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},
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"execution_count": 75,
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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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"# for reproducibility\n",
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"rng = np.random.RandomState(seed=42)\n",
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"\n",
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"std_normal_dist = rng.standard_normal(size=(3, 4, 2))\n",
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"std_normal_dist"
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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": 78,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[[ 0.49671415, -0.1382643 ],\n",
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" [ 0.64768854, 1.52302986],\n",
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" [-0.23415337, -0.23413696],\n",
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" [ 1.57921282, 0.76743473]],\n",
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"\n",
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" [[-0.46947439, 0.54256004],\n",
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" [-0.46341769, -0.46572975],\n",
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" [ 0.24196227, -1.91328024],\n",
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" [-1.72491783, -0.56228753]],\n",
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"\n",
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" [[-1.01283112, 0.31424733],\n",
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" [-0.90802408, -1.4123037 ],\n",
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" [ 1.46564877, -0.2257763 ],\n",
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" [ 0.0675282 , -1.42474819]]])"
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]
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},
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"execution_count": 78,
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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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"# if reproducibility matters ...\n",
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"rng = np.random.RandomState(seed=42)\n",
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"\n",
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"# an array of 10 points randomly sampled from a normal distribution\n",
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"# loc=mean, scale=std deviation\n",
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"rng.normal(loc=0.0, scale=1.0, size=(3, 4, 2))"
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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": 77,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([[[0.37454012, 0.95071431],\n",
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" [0.73199394, 0.59865848],\n",
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" [0.15601864, 0.15599452],\n",
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" [0.05808361, 0.86617615]],\n",
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"\n",
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" [[0.60111501, 0.70807258],\n",
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" [0.02058449, 0.96990985],\n",
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" [0.83244264, 0.21233911],\n",
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" [0.18182497, 0.18340451]],\n",
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"\n",
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" [[0.30424224, 0.52475643],\n",
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" [0.43194502, 0.29122914],\n",
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" [0.61185289, 0.13949386],\n",
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" [0.29214465, 0.36636184]]])"
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]
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},
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"execution_count": 77,
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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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"# uniform distribution\n",
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"rng = np.random.RandomState(seed=42)\n",
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"\n",
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"rng.uniform(low=0, high=1.0, size=(3, 4, 2))"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"- https://docs.scipy.org/doc/numpy/reference/\n",
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"- Your own imagination & dexterity!"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎persist/random-array.npy

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