From cb79fc36071209c64b900a46075c60bb7c2ed065 Mon Sep 17 00:00:00 2001
From: Jeanette Lee <jeanette.lee@stud.hslu.ch>
Date: Fri, 11 Mar 2022 19:12:32 +0000
Subject: [PATCH] Problem 1 solved
---
.../Exercise Sheet - Basics Numpy.ipynb | 40 +++++++++++--------
1 file changed, 23 insertions(+), 17 deletions(-)
diff --git a/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb b/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb
index 5531ef0..bc23d73 100644
--- a/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb
+++ b/notebooks/Block_0/Exercise Sheet - Basics Numpy.ipynb
@@ -16,23 +16,24 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Input as Array: None\n",
- "Input minus min: None\n",
- "Input Array: None\n",
+ "Input as Array: [[-1 2 7]]\n",
+ "Input minus min: [[0 3 8]]\n",
+ "Input Array: [[0. 0.375 1. ]]\n",
"Multiply 1:\n",
- "None\n",
+ "False\n",
"Multiply 2:\n",
- "None\n",
+ "[[14]\n",
+ " [32]]\n",
"Multiply 3:\n",
- "None\n",
- "Mean == None\n"
+ "[[ 9 12 15]]\n",
+ "Mean == 2.6666666666666665\n"
]
}
],
@@ -43,17 +44,19 @@
"def prepare_inputs(inputs):\n",
" # TODO: create a 2-dimensional ndarray from the given 1-dimensional list;\n",
" # assign it to input_array\n",
- " input_array = None\n",
+ " input_array = np.reshape(inputs, (1, len(inputs)))\n",
" \n",
" # TODO: find the minimum value in input_array and subtract that\n",
" # value from all the elements of input_array. Store the\n",
" # result in inputs_minus_min\n",
- " inputs_minus_min = None\n",
+ " min_val = input_array.min()\n",
+ " inputs_minus_min = input_array - min_val\n",
"\n",
" # TODO: find the maximum value in inputs_minus_min and divide\n",
" # all of the values in inputs_minus_min by the maximum value.\n",
" # Store the results in inputs_div_max.\n",
- " inputs_div_max = None\n",
+ " max_val = inputs_minus_min.max()\n",
+ " inputs_div_max = inputs_minus_min / max_val\n",
"\n",
" # return the three arrays we've created\n",
" return input_array, inputs_minus_min, inputs_div_max\n",
@@ -65,18 +68,21 @@
" #\n",
" # Return False if the shapes cannot be used for matrix\n",
" # multiplication. You may not use a transpose\n",
- " pass\n",
- "\n",
+ " if (np.shape(m1)[1] != np.shape(m2)[0]) & (np.shape(m1)[0] != np.shape(m2)[1]):\n",
+ " return False\n",
"\n",
" # TODO: If you have not returned False, then calculate the matrix product\n",
" # of m1 and m2 and return it. Do not use a transpose,\n",
" # but you swap their order if necessary\n",
- " pass\n",
+ " elif (np.shape(m1)[1] == np.shape(m2)[0]):\n",
+ " return np.matmul(m1, m2)\n",
+ " else:\n",
+ " return np.matmul(m2, m1)\n",
" \n",
"\n",
"def find_mean(values):\n",
" # TODO: Return the average of the values in the given Python list\n",
- " pass\n",
+ " return sum(values) / len(values)\n",
"\n",
"\n",
"input_array, inputs_minus_min, inputs_div_max = prepare_inputs([-1,2,7])\n",
@@ -451,7 +457,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -465,7 +471,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.6"
+ "version": "3.9.7"
}
},
"nbformat": 4,
--
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