ASPP 2024 material
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Pietro Berkes
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374
exercises/numpy_broadcasting_extra/broadcasting.ipynb
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374
exercises/numpy_broadcasting_extra/broadcasting.ipynb
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{
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"\n",
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"def print_info(a):\n",
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" \"\"\" Print the content of an array, and its metadata. \"\"\"\n",
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" \n",
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" txt = f\"\"\"\n",
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"dtype\\t{a.dtype}\n",
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"ndim\\t{a.ndim}\n",
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"shape\\t{a.shape}\n",
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"strides\\t{a.strides}\n",
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" \"\"\"\n",
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"\n",
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" print(a)\n",
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" print(txt)"
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]
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"cell_type": "markdown",
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"id": "6cd0f8cf",
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"slide_type": "slide"
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"source": [
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"<font size=9> Mind-on exercises </font>"
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]
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"cell_type": "markdown",
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"id": "acba732f",
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"slide_type": "slide"
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}
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},
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"source": [
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"### Exercise 1: warm up\n",
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"\n",
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"```What is the expected output shape for each operation?```"
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]
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},
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"cell_type": "code",
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"end_time": "2023-06-27T19:58:58.881059Z",
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"start_time": "2023-06-27T19:58:57.830Z"
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"a = np.arange(5)\n",
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"b = 5\n",
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"\n",
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"np.shape(a-b)"
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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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"id": "6f82a2fb",
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"metadata": {
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"end_time": "2023-06-27T19:58:58.884966Z",
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"start_time": "2023-06-27T19:58:57.833Z"
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},
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"slideshow": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"a = np.ones((7, 1))\n",
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"b = np.arange(7)\n",
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"np.shape(a*b)"
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]
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},
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{
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"cell_type": "code",
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"id": "808095ad",
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"metadata": {
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"a = np.random.randint(0, 50, (2, 3, 3))\n",
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"b = np.random.randint(0, 10, (3, 1))\n",
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"\n",
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"np.shape(a-b)"
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]
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},
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"cell_type": "code",
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"a = np.arange(100).reshape(10, 10)\n",
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"b = np.arange(1, 10)\n",
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"\n",
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"np.shape(a+b)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "69632f95",
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"metadata": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Exercise 2\n",
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"\n",
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"```\n",
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"1. Create a random 2D array of dimension (5, 3)\n",
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"2. Calculate the maximum value of each row\n",
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"3. Divide each row by its maximum\n",
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"```\n",
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"\n",
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"Remember to use broadcasting : NO FOR LOOPS!"
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]
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},
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{
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"cell_type": "code",
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"start_time": "2023-06-27T19:58:57.843Z"
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"slide_type": "fragment"
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}
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},
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"outputs": [],
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"source": [
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"## Your code here"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b9facc0f",
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"metadata": {
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"slide_type": "slide"
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},
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"source": [
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"### Exercise 3"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7e8156d0",
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"slide_type": "fragment"
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}
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},
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"source": [
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"Task: Find the closest **cluster** to the **observation**. \n",
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"\n",
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"Again, use broadcasting: DO NOT iterate cluster by cluster"
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]
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},
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"cell_type": "code",
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"id": "2969994e",
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"metadata": {
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"end_time": "2023-06-27T19:58:58.899204Z",
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"start_time": "2023-06-27T19:58:57.847Z"
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"outputs": [],
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"source": [
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"observation = np.array([30.0, 99.0]) #Observation\n",
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"\n",
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"#Clusters\n",
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"clusters = np.array([[102.0, 203.0],\n",
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" [132.0, 193.0],\n",
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" [45.0, 155.0], \n",
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" [57.0, 173.0]])"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"Lets plot this data\n",
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"\n",
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"In the plot below, **+** is the observation and dots are the cluster coordinates"
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]
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},
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"end_time": "2023-06-27T19:58:58.906715Z",
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"start_time": "2023-06-27T19:58:57.850Z"
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},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt \n",
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"\n",
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"plt.scatter(clusters[:, 0], clusters[:, 1]) #Scatter plot of clusters\n",
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"for n, x in enumerate(clusters):\n",
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" print('cluster %d' %n)\n",
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" plt.annotate('cluster%d' %n, (x[0], x[1])) #Label each cluster\n",
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"plt.plot(observation[0], observation[1], '+'); #Plot observation"
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]
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{
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"cell_type": "markdown",
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"id": "4f9b84e2",
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"slide_type": "fragment"
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}
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},
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"source": [
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"Closest cluster as seen by the plot is **2**. Your task is to write a function to calculate this"
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]
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},
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"end_time": "2023-06-26T19:25:08.202848Z",
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"start_time": "2023-06-26T19:25:08.194923Z"
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}
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},
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"source": [
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"\n",
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"**hint:** Find the distance between the observation and each row in the cluster. The cluster to which the observation belongs to is the row with the minimum distance.\n",
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"\n",
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"distance = $\\sqrt {\\left( {x_1 - x_2 } \\right)^2 + \\left( {y_1 - y_2 } \\right)^2 }$"
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]
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"end_time": "2023-06-27T19:58:58.916610Z",
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}
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},
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"outputs": [],
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"source": [
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"## Your code here"
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]
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},
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"cell_type": "markdown",
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"id": "beaee243",
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"source": [
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"## Sources + Resources\n",
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"\n",
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"ASPP 2016 - Stéfan van der Walt - https://github.com/ASPP/2016_numpy\n",
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"\n",
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"Basic Numpy: http://scipy-lectures.org/intro/numpy/index.html\n",
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"\n",
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"Advanced Numpy: http://scipy-lectures.org/advanced/advanced_numpy/index.html\n",
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"\n",
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"Numpy chapter in \"Python Data Science Handbook\" https://jakevdp.github.io/PythonDataScienceHandbook/02.00-introduction-to-numpy.html"
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]
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"name": "python3"
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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"scroll": true
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