diff --git a/subjects/ai/numpy/audit/README.md b/subjects/ai/numpy/audit/README.md index 2c20fb3e..d5295f6d 100644 --- a/subjects/ai/numpy/audit/README.md +++ b/subjects/ai/numpy/audit/README.md @@ -1,31 +1,31 @@ #### Exercise 0: Environment and libraries -##### The exercice is validated if all questions of the exercice are validated +##### The exercise is validated if all questions of the exercise are validated ##### Install the virtual environment with `requirements.txt` ##### Activate the virtual environment. If you used `conda`, run `conda activate ex00` -###### Does the shell specify the name `ex00` of the environment on the left ? +###### Does the shell specify the name `ex00` of the environment on the left? ##### Run `python --version` ###### Does it print `Python 3.8.x`? x could be any number from 0 to 9 -##### Does `import jupyter` and `import numpy` run without any error ? +###### Does `import jupyter` and `import numpy` run without any error? -###### Have you used the followingthe command `jupyter notebook --port 8891` ? +###### Have you used the following command `jupyter notebook --port 8891`? -###### Is there a file named `Notebook_ex00.ipynb` in the working directory ? +###### Is there a file named `Notebook_ex00.ipynb` in the working directory? -###### Is the following markdown code executed in a markdown cell in the first cell ? +###### Is the following markdown code executed in a markdown cell in the first cell? ``` # H1 TITLE ## H2 TITLE ``` -###### Does the second cell contain `print("Buy the dip ?")` and return `Buy the dip ?` in the output section ? +###### Does the second cell contain `print("Buy the dip ?")` and return `Buy the dip ?` in the output section? --- @@ -35,11 +35,11 @@ ##### Add cell and run `type(your_numpy_array)` -###### Is the your_numpy_array an NumPy array ? It can be checked with that should be equal to `numpy.ndarray`. +###### Is the your_numpy_array an NumPy array? It can be checked with that should be equal to `numpy.ndarray`. ##### Run all the cells of the notebook or `python main.py` -###### Are the types printed are as follows ? +###### Are the types printed are as follows? ``` @@ -54,19 +54,17 @@ ##### Delete all the cells you added for the audit and restart the notebook -TODO - --- --- #### Exercise 2: Zeros -##### The exercice is validated is all questions of the exercice are validated +##### The exercise is validated if all questions of the exercise are validated -##### The question 1 is validated is the solution uses `np.zeros` and if the shape of the array is `(300,)` +###### For question 1, does the solution use `np.zeros` and is the shape of the array `(300,)`? -##### The question 2 is validated if the solution uses `reshape` and the shape of the array is `(3, 100)` +###### For question 2, does the solution use `reshape` and is the shape of the array `(3, 100)`? --- @@ -74,15 +72,15 @@ TODO #### Exercise 3: Slicing -##### The exercice is validated is all questions of the exercice are validated +##### The exercise is validated if all questions of the exercise are validated -##### The question 1 is validated if the solution doesn't involve a for loop or writing all integers from 1 to 100 and if the array is: `np.array([1,...,100])`. The list from 1 to 100 can be generated with an iterator: `range`. +###### For question 1, is validated if the solution doesn't involve a for loop or writing all integers from 1 to 100 and if the array is: `np.array([1,...,100])`. The list from 1 to 100 can be generated with an iterator: `range`. Were the previous requirements fulfilled? -##### The question 2 is validated if the solution is: `integers[::2]` +###### For question 2, is the solution `integers[::2]`? -##### The question 3 is validated if the solution is: `integers[::-2]` +###### For question 3, is the solution `integers[::-2]`? -##### The question 4 is validated if the array is: `np.array([0, 1,0,3,4,0,...,0,99,100])`. There are at least two ways to get this results without for loop. The first one uses `integers[1::3] = 0` and the second involves creating a boolean array that indexes the array: +###### For question 4, is the array `np.array([0, 1,0,3,4,0,...,0,99,100])`? There are at least two ways to get this results without for loop. The first one uses `integers[1::3] = 0` and the second involves creating a boolean array that indexes the array: ```python mask = (integers+1)%3 == 0 @@ -95,15 +93,15 @@ integers[mask] = 0 #### Exercise 4: Random -##### The exercice is validated is all questions of the exercice are validated +##### The exercise is validated if all questions of the exercise are validated ##### For this exercise, as the results may change depending on the version of the package or the OS, I give the code to correct the exercise. If the code is correct and the output is not the same as mine, it is accepted. -##### The question 1 is validated if the solution is: `np.random.seed(888)` +###### For question 1, is the solution `np.random.seed(888)`? -##### The question 2 is validated if the solution is: `np.random.randn(100)`. The value of the first element is `0.17620087373662233`. +###### For question 2, is the solution `np.random.randn(100)`? The value of the first element is `0.17620087373662233`. -##### The question 3 is validated if the solution is: `np.random.randint(1,11,(8,8))`. +###### For question 3, is the solution `np.random.randint(1,11,(8,8))`? ```console Given the NumPy version and the seed, you should have this output: @@ -118,7 +116,7 @@ integers[mask] = 0 [ 4, 4, 9, 2, 8, 5, 9, 5]]) ``` -##### The question 4 is validated if the solution is: `np.random.randint(1,18,(4,2,5))`. +###### For question 4, is the solution `np.random.randint(1,18,(4,2,5))`? ```console Given the NumPy version and the seed, you should have this output: @@ -142,15 +140,15 @@ integers[mask] = 0 #### Exercise 5: Split, concatenate, reshape arrays -##### The exercice is validated is all questions of the exercice are validated +##### The exercise is validated if all questions of the exercise are validated -##### The question 1 is validated if the generated array is based on an iterator as `range` or `np.arange`. Check that 50 is part of the array. +###### For question 1, is the generated array based on an iterator as `range` or `np.arange`? Check that 50 is part of the array. -##### The question 2 is validated if the generated array is based on an iterator as `range` or `np.arange`. Check that 100 is part of the array. +###### For question 2, is the generated array based on an iterator as `range` or `np.arange`? Check that 100 is part of the array. -##### The question 3 is validated if the array is concatenated this way `np.concatenate(array1,array2)`. +###### For question 3, is the array concatenated this way `np.concatenate(array1,array2)`? -##### The question 4 is validated if the result is: +###### For question 4, is the result the following? ```console array([[ 1, ... , 10], @@ -168,13 +166,13 @@ https://jakevdp.github.io/PythonDataScienceHandbook/ (section: The Basics of Num #### Exercise 6: Broadcasting and Slicing -##### The exercice is validated is all questions of the exercice are validated +##### The exercise is validated if all questions of the exercise are validated -##### The question 1 is validated if the output is the same as: +###### For question 1, is the output the same as the following? `np.ones([9,9], dtype=np.int8)` -##### The question 2 is validated if the output is +###### For question 2, is the output the following? ```console array([[1, 1, 1, 1, 1, 1, 1, 1, 1], @@ -205,9 +203,9 @@ Here is an example of a possible solution: #### Exercise 7: NaN -##### The exercice is validated is all questions of the exercice are validated +##### The exercise is validated if all questions of the exercise are validated -##### This question is validated if, without having used a for loop or having filled the array manually, the output is: +###### Without having used a for loop or having filled the array manually, is the output the following? ```console [[ 7. 1. 7.] @@ -244,13 +242,11 @@ There are two steps in this exercise: #### Exercise 8: Wine -##### The exercice is validated is all questions of the exercice are validated - -##### This question is validated if the text file has successfully been loaded in a NumPy array with +##### The exercise is validated if all questions of the exercise are validated -`genfromtxt('winequality-red.csv', delimiter=',')` and the reduced arrays weights **76800 bytes** +###### Has the text file successfully been loaded in a NumPy array with `genfromtxt('winequality-red.csv', delimiter=',')` and the reduced arrays weights **76800 bytes**? -##### This question is validated if the output is +###### Is the output the following? ```python array([[ 7.4 , 0.7 , 0. , 1.9 , 0.076 , 11. , 34. , @@ -263,11 +259,11 @@ array([[ 7.4 , 0.7 , 0. , 1.9 , 0.076 , 11. , 34. , This slicing gives the answer `my_data[[1,6,11],:]`. -##### This question is validated if the answer if False. There many ways to get the answer: find the maximum or check values greater than 20. +###### Is the answer False? There are many ways to get the answer: find the maximum or check values greater than 20. -##### This question is validated if the answer is 10.422983114446529. +###### Is the answer 10.422983114446529? -##### This question is validated if the answers is: +###### Is the answer the following? ```console pH stats @@ -281,9 +277,9 @@ This slicing gives the answer `my_data[[1,6,11],:]`. > *Note: Using `percentile` or `median` may give different results depending on the duplicate values in the column. If you do not have my results please use `percentile`.* -##### This question is validated if the answer is ~`5.2`. The first step is to get the percentile 20% of the column `sulphates`, then create a boolean array that contains `True` of the value is smaller than the percentile 20%, then select this rows with the column quality and compute the `mean`. +###### Is the answer ~`5.2`? The first step is to get the percentile 20% of the column `sulphates`, then create a boolean array that contains `True` of the value is smaller than the percentile 20%, then select this rows with the column quality and compute the `mean`. -##### This question is validated if the output for the best wines is: +###### Is the output for the best wines the following? ```python array([ 8.56666667, 0.42333333, 0.39111111, 2.57777778, 0.06844444, @@ -291,7 +287,7 @@ array([ 8.56666667, 0.42333333, 0.39111111, 2.57777778, 0.06844444, 12.09444444, 8. ]) ``` -##### This question is validated if the output for the bad wines is: +###### Is the output for the bad wines the following? ```python array([ 8.36 , 0.8845 , 0.171 , 2.635 , 0.1225 , 11. , @@ -306,7 +302,7 @@ This can be done in three steps: Get the max, create a boolean mask that indicat #### Exercise 9: Football tournament -##### This exercise is validated if the output is: +###### Is the output the following? ```console [[0 3 1 2 4]