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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Extra Notebook\n",
"Containing exercises to test your new data science skills\n",
"\n",
"## Exercise 1\n",
"Create a 8x8 matrix with a checkboard pattern of 1s and 0s"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 2\n",
"Obtain the determinant of a [Cauchy matrix](https://en.wikipedia.org/wiki/Cauchy_matrix) from two arrays.\n",
"\n",
"Remember the cauchy formula:\n",
"$$ C_{{ij}}={\\frac {1}{x_{i}-y_{j}}} $$"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
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},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 3\n",
"Remember the canvas exercises? Can you use slicing with a skip of 5 to get the image below?\n",
"\n",
"Remember how you can add a step to slicing in the Python built-in data types? Well, you can do that in NumyPy as well!\n",
"\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 4\n",
"Consider a random vector with shape (100, 2) representing coordinates, find the maximum euclidian distance between 2 points.\n",
"\n",
"Recall the Euclidean distance formula: the distance from $(x_1, y_1)$ to $(x_2, y_2)$ is\n",
"\n",
"$$ \\sqrt{(x_2 - x_1)^2 + (y_2 - y_1)^2}$$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Execise 5\n",
"Generate a generic 2D Gaussian-like array, centered around 0,0\n",
"\n",
"You might want to recall the gaussian formula:\n",
"\n",
"$$ P(x) = \\frac{1}{\\sigma \\sqrt{2\\pi}} e^{\\frac{-(x - \\mu)^2}{2\\sigma^2}} $$\n",
"\n",
"You can use $$ \\mu = 0 \\quad \\sigma = 1 $$\n",
"\n",
"*Hint: You can find the [np.meshgrid](https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.meshgrid.html) function helpful here.*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise 6\n",
"The file `data/soft_survey.csv` conta"
]
},
{
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"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
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"name": "ipython",
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