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Commit 9d606fe8 authored by Jonathan Poalses's avatar Jonathan Poalses
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refactoring

parent df80236c
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numbers_ml.ipynb
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......@@ -24,7 +24,7 @@
},
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"cell_type": "code",
"execution_count": 928,
"execution_count": 973,
"outputs": [],
"source": [
"# Importing pyplot so we can visualize things\n",
......@@ -78,8 +78,8 @@
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......@@ -95,13 +95,13 @@
},
{
"cell_type": "code",
"execution_count": 929,
"execution_count": 974,
"outputs": [
{
"data": {
"text/plain": "array([0, 1, 2, ..., 8, 9, 8])"
},
"execution_count": 929,
"execution_count": 974,
"metadata": {},
"output_type": "execute_result"
}
......@@ -116,15 +116,15 @@
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"cell_type": "markdown",
"source": [
"As can be seen, the digits dataset contains images labelled with the integers 0 through 9."
"As can be seen, the digits dataset contains single dimensional arrays containing the integers 0 through 9, representing the class of the associated image."
],
"metadata": {
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......@@ -132,13 +132,13 @@
},
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"cell_type": "code",
"execution_count": 930,
"execution_count": 975,
"outputs": [
{
"data": {
"text/plain": "array([[[ 0., 0., 5., ..., 1., 0., 0.],\n [ 0., 0., 13., ..., 15., 5., 0.],\n [ 0., 3., 15., ..., 11., 8., 0.],\n ...,\n [ 0., 4., 11., ..., 12., 7., 0.],\n [ 0., 2., 14., ..., 12., 0., 0.],\n [ 0., 0., 6., ..., 0., 0., 0.]],\n\n [[ 0., 0., 0., ..., 5., 0., 0.],\n [ 0., 0., 0., ..., 9., 0., 0.],\n [ 0., 0., 3., ..., 6., 0., 0.],\n ...,\n [ 0., 0., 1., ..., 6., 0., 0.],\n [ 0., 0., 1., ..., 6., 0., 0.],\n [ 0., 0., 0., ..., 10., 0., 0.]],\n\n [[ 0., 0., 0., ..., 12., 0., 0.],\n [ 0., 0., 3., ..., 14., 0., 0.],\n [ 0., 0., 8., ..., 16., 0., 0.],\n ...,\n [ 0., 9., 16., ..., 0., 0., 0.],\n [ 0., 3., 13., ..., 11., 5., 0.],\n [ 0., 0., 0., ..., 16., 9., 0.]],\n\n ...,\n\n [[ 0., 0., 1., ..., 1., 0., 0.],\n [ 0., 0., 13., ..., 2., 1., 0.],\n [ 0., 0., 16., ..., 16., 5., 0.],\n ...,\n [ 0., 0., 16., ..., 15., 0., 0.],\n [ 0., 0., 15., ..., 16., 0., 0.],\n [ 0., 0., 2., ..., 6., 0., 0.]],\n\n [[ 0., 0., 2., ..., 0., 0., 0.],\n [ 0., 0., 14., ..., 15., 1., 0.],\n [ 0., 4., 16., ..., 16., 7., 0.],\n ...,\n [ 0., 0., 0., ..., 16., 2., 0.],\n [ 0., 0., 4., ..., 16., 2., 0.],\n [ 0., 0., 5., ..., 12., 0., 0.]],\n\n [[ 0., 0., 10., ..., 1., 0., 0.],\n [ 0., 2., 16., ..., 1., 0., 0.],\n [ 0., 0., 15., ..., 15., 0., 0.],\n ...,\n [ 0., 4., 16., ..., 16., 6., 0.],\n [ 0., 8., 16., ..., 16., 8., 0.],\n [ 0., 1., 8., ..., 12., 1., 0.]]])"
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"execution_count": 975,
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......@@ -150,8 +150,8 @@
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......@@ -176,7 +176,7 @@
},
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"cell_type": "code",
"execution_count": 931,
"execution_count": 976,
"outputs": [
{
"data": {
......@@ -203,15 +203,15 @@
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"end_time": "2023-05-26T14:17:35.781215Z",
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"cell_type": "markdown",
"source": [
"Unfortunately, this data cannot immediately be used with a classifier, for that we would need to flatten the images, turning it from an 8x8 two dimensional array, into a 64 long one dimensional array."
"Unfortunately, this data cannot immediately be used with a classifier, as while it is an 8x8 2D array, the labels are one dimensional arrays; For that we would need to flatten the images, turning it from an 8x8 two-dimensional array, into a 64 long one dimensional array."
],
"metadata": {
"collapsed": false
......@@ -219,13 +219,13 @@
},
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"cell_type": "code",
"execution_count": 932,
"execution_count": 977,
"outputs": [
{
"data": {
"text/plain": "array([[ 0., 0., 5., ..., 0., 0., 0.],\n [ 0., 0., 0., ..., 10., 0., 0.],\n [ 0., 0., 0., ..., 16., 9., 0.],\n ...,\n [ 0., 0., 1., ..., 6., 0., 0.],\n [ 0., 0., 2., ..., 12., 0., 0.],\n [ 0., 0., 10., ..., 12., 1., 0.]])"
},
"execution_count": 932,
"execution_count": 977,
"metadata": {},
"output_type": "execute_result"
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......@@ -238,40 +238,40 @@
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"end_time": "2023-05-26T14:17:35.788274Z",
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},
{
"cell_type": "code",
"execution_count": 933,
"execution_count": 978,
"outputs": [
{
"data": {
"text/plain": "True"
},
"execution_count": 933,
"execution_count": 978,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check to see if the first dimension of the data tuples match\n",
"# Check to see if the first dimension of the data shape tuples match\n",
"(flat_images.shape[0] == data.target.shape[0])"
],
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"cell_type": "markdown",
"source": [
"As can be seen, the image data now matches the target data in size, both being a 1797 long set of arrays."
"As can be seen, the image data now matches the target data in size, both being a 1797 long set of one dimensional arrays."
],
"metadata": {
"collapsed": false
......@@ -317,7 +317,7 @@
},
{
"cell_type": "code",
"execution_count": 934,
"execution_count": 979,
"outputs": [],
"source": [
"# We'll start by splitting the data into training and testing, going with a 75% train, 25% test split, a 50/50 split, and a 25% train 75% test split.\n",
......@@ -329,8 +329,8 @@
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......@@ -345,7 +345,7 @@
},
{
"cell_type": "code",
"execution_count": 935,
"execution_count": 980,
"outputs": [],
"source": [
"# First the Gaussian Bayes\n",
......@@ -378,8 +378,8 @@
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......@@ -394,7 +394,7 @@
},
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"execution_count": 981,
"outputs": [
{
"name": "stdout",
......@@ -423,14 +423,14 @@
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"execution_count": 937,
"execution_count": 982,
"outputs": [
{
"name": "stdout",
......@@ -459,14 +459,14 @@
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"execution_count": 938,
"execution_count": 983,
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"name": "stdout",
......@@ -495,22 +495,22 @@
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"cell_type": "code",
"execution_count": 939,
"execution_count": 984,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8377777777777777\n",
"0.8487208008898777\n",
"0.7789317507418397\n"
"0.8711111111111111\n",
"0.8286985539488321\n",
"0.7648367952522255\n"
]
}
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......@@ -531,22 +531,22 @@
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"cell_type": "code",
"execution_count": 940,
"execution_count": 985,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9844444444444445\n",
"0.9599555061179088\n",
"0.9473293768545994\n"
"0.98\n",
"0.9655172413793104\n",
"0.9443620178041543\n"
]
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......@@ -567,14 +567,14 @@
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"execution_count": 986,
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......@@ -603,8 +603,8 @@
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......@@ -628,7 +628,7 @@
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......@@ -750,8 +750,8 @@
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......
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