Commit 2d23bfdf authored by Simon Clarke's avatar Simon Clarke
Browse files

Fixed typos

parent bbc790bf
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# Classification using k Nearest Neighbours
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Previously we have considered regression problems for Machine Learning, where the target value is a continuous variable. The other primary type of supervised learning problem is classification problems. For example, a common Machine Learning problem is that we are given a number of images of animals, and we need to classify them into the various animals such as cats, dogs and horses. Another type of problem is the 'Churn problem', for which the object is to predict whether a customer will continue with a company based on their past activities. This is known as a binary classification problem, since there is only the two outcomes that either the customer will continue or they will not.
One of the simplest classification algorithms is k-Nearest Neighbours (kNN). Consider that our dataset consists of m continuous features and 1 discrete target value, and we have n instances or measurements. Then the features can be plotted in 'feature space'. For two features this corresponds to the plane and for three features to three-dimensional space. Each pair of instances will be separated by a distance d. This is know as the Euclidean distance. If we are trying to predict the label for a particular instance the algorithm then finds the k nearest neighbours and records the target values for these neighbours. Then the most common target value is chosen as the label for that particular instance. This is known as 'hard' voting. An alternative form of harde voting is where the votes are weighted by the distance away from the instance, and therefore closer points have more importance.
One of the simplest classification algorithms is k-Nearest Neighbours (kNN). Consider that our dataset consists of m continuous features and 1 discrete target value, and we have n instances or measurements. Then the features can be plotted in 'feature space'. For two features this corresponds to the plane and for three features to three-dimensional space. Each pair of instances will be separated by a distance d. This is know as the Euclidean distance. If we are trying to predict the label for a particular instance the algorithm then finds the k nearest neighbours and records the target values for these neighbours. Then the most common target value is chosen as the label for that particular instance. This is known as 'hard' voting. An alternative form of hard voting is where the votes are weighted by the distance away from the instance, and therefore closer points have more importance.
In this lesson we will use the Iris Dataset, which is a famous dataset which classifies three species of iris based on the dimensions of the flowers. We will also show how functions can be defined to repeat tasks.
In this lesson we will use the [Iris Dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), which is a famous dataset which classifies three species of iris based on the dimensions of the flowers. We will also show how functions can be defined to repeat tasks.
We first import libraries that will be required later.
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``` python
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns;
import pandas as pd
```
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## Contents
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* Iris Dataset
* kNN Algorithm
* Confusion Matrix and Accuracy
* Normalization
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## Iris Dataset
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The Iris Dataset can be loaded using seaborn. If we print the shape there are are 150 instances and for each instance there are 4 features and 1 label. The features correspond to measurements of the flowers, and the target value is the species.
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``` python
iris = sns.load_dataset('iris') # load the dataset from seaborn
print('Shape of the iris dataset is',iris.shape) # display the shape of the data
iris.head() # display the first few lines
```
%%%% Output: stream
Shape of the iris dataset is (150, 5)
%%%% Output: execute_result
sepal_length sepal_width petal_length petal_width species
0 5.1 3.5 1.4 0.2 setosa
1 4.9 3.0 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
3 4.6 3.1 1.5 0.2 setosa
4 5.0 3.6 1.4 0.2 setosa
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We can view the descriptive statistics of the numerical columns using `df.describe()`.
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``` python
iris.describe() # show the statistics of the numerical columns
```
%%%% Output: execute_result
sepal_length sepal_width petal_length petal_width
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.057333 3.758000 1.199333
std 0.828066 0.435866 1.765298 0.762238
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000
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We can also see the unique values of the species label. In this case there are three types of species or labels. We will be attempting to predict these labels, though it is easier to work with equivalent numerical labels.
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``` python
iris['species'].unique() # show the unique values of the species column
```
%%%% Output: execute_result
array(['setosa', 'versicolor', 'virginica'], dtype=object)
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We will try to predict the species using only the sepal length and sepal width, therefore it is helpful to plot how the labels vary with these features. As can be seen the setosa species is fairly well separated from the other two species, but the boundary between versicolor and viginica is fairly fuzzy.
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``` python
sns.scatterplot(x="sepal_length", y="sepal_width", hue='species', data=iris);
```
%%%% Output: display_data
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAABaAElEQVR4nO3dd3xb1fn48c+xLe+9VxzH2cPOnmwoAUKAAgFCoTSMAoVAKIQCX1oIBUr5FcooEKCMsEkadtgBAgQyneXs4TiJ95Zt2bI1zu+PKw9Zciw5siUn5/16+RXr6N6rx9eOHt17znmOkFKiKIqiKH7eDkBRFEXxDSohKIqiKIBKCIqiKIqNSgiKoigKoBKCoiiKYqMSgqIoigL0UUIQQvgLITYLIVY4ee50IYReCLHF9vVAX8SkKIqi2Avoo9dZAOwCIrt4/mcp5ew+ikVRFEVxotcTghAiHTgfeBS40xPHjI+Pl5mZmZ44lKIoygkjNze3UkqZ0NXzfXGF8DTwFyDiKNtMF0JsBYqBhVLKHUc7YGZmJhs3bvRchIqiKCcAIcShoz3fq30IQojZQLmUMvcom20CBkopxwL/AT7u4lg3CiE2CiE2VlRUeD5YRVGUE1xvdyqfBFwohCgA3gfOFEK83XEDKWWdlLLB9v0XgE4IEd/5QFLKl6WUk6SUkxISurziURRFUXqoVxOClPI+KWW6lDITmAt8L6W8uuM2QohkIYSwfT/FFlNVb8alKIqiOOqrUUZ2hBA3A0gpXwTmAH8SQpiBJmCuVCVYFUVR+pzoj++9kyZNkqpTWVEUxT1CiFwp5aSunvfKFYKieIrFKtlTWkdBlYHo0EBGJkcSExbo7bAUpV9SCUHp11btKefGt3KxWLUr3YvHp/LA7FHEhAV5OTJF6X9ULSOl3yqvM3L/R9vbkgHAR5uL2VlS78WoFKX/UglB6bcams2U1hkd2qsMzV6IRlH6P5UQlH4rMTKI6Vmxdm1CwKD4cC9FpCj9m0oISr8VHqRj0YVjmJQZA0BsWCDP/24Cw5OOViVFUZSuqE5lpV8bnhzB6/MmU1pnJCwwgNToEG+HpCj9lkoISr8XEawjIljn7TAUpd9Tt4wURVEUQCUERVEUxUYlBEVRFAVQCUFRFEWxUQlBURRFAVRCUBRFUWxUQlAURVEAlRAURVEUG5UQFK+SUlJeZ6SuyeTtUBTlhKdmKiteU1TTxHvrD/Hu+iMkRwVz33kjmJ4VR4C/+pyiKN6g/ucpXmG1St5eV8BzPxyg2tDCzuI65r2+gR3Fdd4OTVFOWCohKF5RVm/k7TWH7dq05TDV4jaK4i0qISheERTgT1yE49rHYUH+XohGURRQCUHxktiwQO6fNQoh2tsGx4eRnR7ttZgU5USnOpUVrzltWDzLb5rO9uI6okN1jBsQTUZsqLfDUpQTlkoIitcEBvgzMTOWiZmx3W+sKEqvU7eMFEVRFEBdIShdOFRlYNWecnIP1XD68EROGhxHUpRanlJRjmcqISgOKuqNzH93M3lFegA+3VrC3MkDWHThKIJ16k9GUY5X6paR4mBfeUNbMmi1dOMRCqoavRSRoih9QSUExYHFKh3apNTqDimKcvxSCUFxMCQhnMw4++Gf545JJiMuzEsRKYrSF9QNYcVBSnQI/71mEh9uKmJNfhWzspM5b0wKYYHqz0VRjmfqf7ji1NCkCO45bwQtFguB/qqchKKcCPrklpEQwl8IsVkIscLJc0II8awQYr8QYpsQYkJfxKS4RiUDRTlx9NUVwgJgFxDp5LnzgKG2r6nAYtu/iuIRRTVN7CjWYzRZGJ4cwfBkZ3+GiqL0ekIQQqQD5wOPAnc62eQi4E2pDWFZK4SIFkKkSClLejs25fhXUGXghiUb2V/RAECwzo93bpjKxIGqXIaidNYXt4yeBv4CWLt4Pg040uFxoa1NUY7ZhoPVbckAwGiy8szK/TS1WLwYlaL4pl5NCEKI2UC5lDL3aJs5aXMY8C6EuFEIsVEIsbGiosJjMSrHt+LaJoe2/MoGmkwqIShKZ719hXAScKEQogB4HzhTCPF2p20KgQEdHqcDxZ0PJKV8WUo5SUo5KSEhobfiVY4zzm4NXTYxndgwx8V5FOVE16sJQUp5n5QyXUqZCcwFvpdSXt1ps0+Ba2yjjaYBetV/oHjKuIxoHr80h+hQHTp/wTXTBzJnYrq3w1IUn+SVeQhCiJsBpJQvAl8As4D9QCNwrTdiUo5P4UEBXDF5AKcNS8BksZAcFYLOX03QVxRn+iwhSClXAats37/YoV0Ct/ZVHMqJKTkq2NshKIrPUzOVlV7T2GxmV0kd1Y0tDIwLY1hShLdDUhTlKFRCUHpFjaGZt9Ye4tnv9mO2ShLCg3jqirGcPFQNCFAUX6Vupiq9Yluhnn9/uw+zrZR2RUMzD366k8IataaCovgqlRCUXlHoZPz/gYoGKuqbvRCNoiiuUAlB6RWpTtZfzowLJT4syAvRKIriCpUQlF6RnRbFracPxs82Dz0qRMeiC0czoNPCO4qi+A7Vqaz0iviIIG4+bTCnDkug2tDCwLhQRqVGeTssRVGOQiUEpddEhOiYmhXn7TAURXGRumV0AttVomdPaZ23wzhmzWYLhTWN1DS2eDsURXHKZDVR0lBCdVN1j4/RYmmhuKGYWmOt5wLrRF0hnID2ltXx6ZYS3vi1gAB/wY2nZjFrTAoD48O8HZrb8isa+M93+/hsWwmZ8aEsumA0MwbH4+fnrIiuovS9wvpCluxYwkf7PiI+JJ57p9zLyWkno/PXuXyMAn0BL217ia8OfkVGZAb3TrmXqSlT8ROe/UyvrhBOQL/sr+K5H/ZT32ymptHE41/tYcOhnn9y8ZamFgv//HI3H20pxmyV7C83cO2SDewpq/d2aIoCgNlq5u2db7N0z1JarC0UG4pZ8MMCdlbvdPkYTeYm/p37b1bkr8AszeTr87nlu1vYV7PP4/GqhHCCMbaY+XybYzHZlTvLvRDNsSnRN/HNzjK7NpNF2i2IoyjeVNlUyUf7P7Jrk0gO1Bxw+RhlhjJ+OPKDXZvZauag/qBHYuxIJYQTjM5fkB7jOEdgQKxjm68LCfR3uq5BVLDrl+KK0ptC/ENIDkt2aI8Mcn1d75CAEGKCYhyPEej5tcFVQjjB+Pv7c9mkdMKD2ruPYsMCOWtkkhej6pmUqBAWXTDKru2UIfGMTPH8fxRF6Ymo4Cjunny33b3+kbEjGRU36ih72UsKS+LeqffatU1Pmc6w2GEei7OV0KpP9y+TJk2SGzdu9HYY/dq6/Cr2lNXjLwQjkiOYmNk/F51vNlnYXqznYIWBmLBAxqRGkuRklrSieIvZamZ39W4O1B4gPDCcUbGjSAlPcesYzeZmdlXvoqCugJigGEbGjSQxNNHtWIQQuVLKSV0+rxKCoijKiaG7hKBuGSmKoiiAmodwQjKaLGwoqObddYcJDPDjd1MzmJAR49bSkg1GM2vzq3h/w2Hiw4O4YvIAxg2IRoj28f97Suv4ZEsx2wr1XDQuldOGJZAYqVYuUxRfpRLCCWj9wWqueW192+PPthaz9MbpTB7kej/Cqr3lzH93c9vjDzcVsfzm6eQMiAbgcJWB37+2nvI6rdz16v2V3HxaFgtnDidArWmsKD5J/c88wViskiW/Fti1WSV8urXI5WM0GE08/8N+u7YWi5U1+VVtj/eU1bclg1avrS5wuk6Coii+QSWEE4wAnFV16Hirp9tjCO04ztrtX8n56yuK4ptUQjjB+PkJ/jAj067N309w4dhUl48RFqRj/hlD7dqCAvyYnhXf9nhEcgTJUfaL4dxwyiDSY9R6CIriq9Sw0xNQs8lC7uEalm44QlCAH5dNGsD4AdFu3ds3NJtZf7Ca/208QnxEEJeMT2Ns507lsnq+2FbCtiI9F+SkcMrQeBIiVKeyoniLmoegKIqiAGoegqIoiuIiNezUg4wmC9uL9BwobyAmLJDs9ChSeqGMwoHyBnYU6zFZJaNTIxmRbF+7p7axhbxCPYW1TaRFh5CTHkV0qGMROEXpz/RGPTuqdlBiKCElLIVR8aOIDor2dlj9mkoIHvTV9lLuWLql7fH0rFiemTveo5OxdpfUceV/11LTaAIgNNCf9/44jbG28f9Gk5nFPx7gpR/z2/a54eRB3DVzGCGB6tetHB+MZiOvbn+V13e83tZ2zahruG38bQQHqH6qnlK3jDykpLaJhz7bYde2Jr+aXSWeXaLy251lbckAoLHFwltrCrBatb6g/IpGXv4p326fV385SH6FwaNxKIo3FdQVsGTHEru2t3a+1StrBJxIVELwkCaThdomk0N7fbPZo69zqMrxjT2/yoBZWgFoaDbTeZyAlFq7ohwvDCYDEvs/dInEYFIffI6FSggekhIVwjmj7BfCCPT3Y0hiuEdf59xsx7K5v5uSQaC/PwAZcSGkRtlfMidHBpMRp8b/K8eP9Ih0h4VnkkKTGBAxwEsRHR9UQvCQkEB/7jlvOJdNTCdY58fIlAiWXDeZ4UkRHn2dKZmx/POSbBIigogO1fF/s0Zy+vD2uujJkSH89w+TOGVoPEEBfpw8JJ5X/jCpVzq3FcVbkkKTePaMZ5mROoNAv0Cmp0znP2f+h6Sw/rfQky9R8xA8zGS2UtHQTFiQP1EhvTeyp6LeiFVCUhcd1oYWM/pGE1GhOsJUZ7JynGo0NaJv0RMVGEWoTl0Fd6e7eQjqncLDdAF+pEb3/qfx7mb8hgUGqESgHPdCdaEqEXhQr75jCCGCgZ+AINtrLZdSPthpm9OBT4DW4QEfSin/3ptxHe9azFa2F+kp1jeRGhXC6JQIgjolh0OVBvaW1ePnpy2hmdapxlCL2UJ+hYHKhmZSo0MYFB/mVgE8VxXVNHGoykBYcACDE8Lt1npWFKVvufW/TwgxA8jsuJ+U8s2j7NIMnCmlbBBC6IDVQogvpZRrO233s5RytjuxKM6ZzVY+2FTIg5/soMViRecvWHThaC6bOIDAAK3LaNuRWhYu38resgYAJmRE8/BvxzA6NQrQah0t3XiEhz7bicUqCdb58fzvJnDWSM/en80rrOW6JRupaNDKZF89NYM/zxxGXFhQN3sqitIbXO5UFkK8BTwBnAxMtn11eS8KQGoabA91tq/+12nRj2wv1rPoUy0ZAJgskr9/tpMdxfq2bT7bVtyWDAA2Ha7lp70VbY/3lTfw4Kc7sNjmNhhNVu7631YKaxo9Fqeh2cxjX+5uSwYAb687TF6h/ih7KYrSm9y5QpgEjJJu9kILIfyBXGAI8LyUcp2TzaYLIbYCxcBCKeWOzhsIIW4EbgTIyMhwJ4QTSnl9M81mq11bs9lKWZ1R+95kIfdQjcN+mw/Xth+jzugwl6G20US1ocVj5av1TSY2HXaMo6TW6JHjK4riPneGnW4HkrvdqhMppUVKOQ5IB6YIIcZ02mQTMFBKORb4D/BxF8d5WUo5SUo5KSEhwd0wThgpUcGEBvrbtYXo/Ns6uoN0/swYHO+w39QOy2emRofg32kVnYTwIBIiPHcrJyY0kJOcxDEgVg2PVRRv6TYhCCE+E0J8CsQDO4UQXwshPm39cvWFpJS1wCrg3E7tda23laSUXwA6IYTjO4XiktGpkfzzkmwibJ2z4UEBPHbJGMakthfAO3dMElMyY9oe/2ZkIicPbU+ygxPDeWJODsE67c8jOlTHM3PHeXQuQ0igPwvPGU5WvHbF4Sfg9jOHkJ0W7bHXUBTFPd3OQxBCnHa056WUPx5l3wTAJKWsFUKEAN8Aj0spV3TYJhkok1JKIcQUYDnaFUOXgfnyPARfsaNIT2mdkeSo4LbO4o5K9U3sK2/A308wPDGCuE6f/q1WSUGVgWpDC8lRwb220lllfTOHqxsJDfRnUHwYQTr/7ndSFKVHjnkeQusbvhDicSnlPZ0O/jjQZUIAUoA3bP0IfsAyKeUKIcTNtmO/CMwB/iSEMANNwFx3+ykUR6PTohid5pgIWiVHhZB8lE/8fn6CrIRwsnr57lx8RBDxHrwVpShKz7k8U1kIsUlKOaFT2zYpZU6vRHYUvnyFYGgxU17XTFigf4/LXlfUGSmtMxITGkh6bM8+mZfUNlLZ0EJceCCp0Y7HsFglRTWN+PkJ0qJDnM4xqGpopq7JRHx4EBEhuh7FUVDZQL3RQlpMMLH9eTiplKA/AtIKUQPAz/0rGYvVQomhBCEEqWGpTs95jbGGupY6YoNjiQj0bNkTRTnmKwQhxJ+AW4AsIcS2Dk9FAL8ce4jHj33l9TyyYhc/7q0gOTKYR347htOHJ7i1VvGGgmoeXbGTLYV6MmJDeWD2SM4ckYifn+vH+GV/JQ99toO9ZQ0MTQznwQtG2fURlNUZWfLrQV79uQB/P8FtZw5h7pQBdm/Yaw5Uct9HeRRUNjIxI4a//3a001tPXWluMfPNrnL+8cUuSvRGpmXFcu+5IxiXEdP9zr6msRo2vQk/Pg5WM0z7E0z9E0Q6FhrsSnljOe/uepc3d75JgF8AN+bcyKVDLyUmuP18rC9Zz0NrHuJw/WHGJozl/qn3MzJuZG/8RIrilCvvMu8CFwCf2v5t/Zoopby6F2PrVwzNZh7+bCc/2sbzl9YZuentXPaU1rt8jMIaA/d+kMcW21j8w9WNzH9vM9uKXB+bv7e0ngXvb26bZ7CvvIEF72+xi+P73WUsXpVPi8VKk8nC//t6D2vzq9uez69o4LolGymo1OYd5B6u4fb3NlPdYc5Ad7YW6rlj6RZK9Now0rX51Tz25S6q6l0/hs8oWA0rHwRTI1ha4JdnYO+Xbh3ip8KfeHX7q5isJprMTTyz6Rk2lG5ofwl9AfO/n8/h+sMAbK3Yyj0/3UN1U3VXh1QUj3MlIfgDdcCtQH2HL4QQsUfZ74RSVmfkp32Vdm0WqyS/0vX67IeqmjhQ0WDXZjRZOejG4jaHqg1UNrTYtVUZWtrWUTBbrCzPLXLY76vtJR3iaKTJZLF7/kCFgaLaJpfjOFDZ0DaxrdW6gzUc8eDktj6z53PHts1va8nBBWarmU/2f+LQ/t2h79q+P1J/hCaz/fk9WHeQEkNJ590Upde4khBygY22fyuAvcA+2/e5vRda/xIWFOB0nH6MG2sZRwbrCHEyyiYmzPVjRIcG0mkKAX5CGzoKEODvx+jUSIf9hndYl7l1246CdX5u1RlytoZzfHggEcH9sFZRgpPbNiljwc+1fpUAvwBGxjoeY2jM0LbvI4McfyfB/sGE6zy7noaiHE23CUFKOUhKmQV8DVwgpYyXUsYBs4EPezvA/iIpMphHLhpDx37Cc8ckMSLF9Y7B0akRLDxnmF3bpRPSGOXGMUYmRfCn0wbbtd14ahYjO7zhXzF5gN2bfnJUEDNHtdcpGpoYzrwZmXbH+NvsUQyMC3M5jtGpkZw5or3fQgi477wRDE7shx2lw8+DyLT2xyExMHEeuFHs75JhlxAV1N4HkxSaxBkZZ7Q9Hhw1mN+N+J3dPndPvpsBkWrBF6XvuDPKKFdKObFT28aj9Vj3Fl8dZWSyWNldUsfBSgMxoYGMTI0kPty9kTW1Tc1sO6LncHUT8eFB5KRFkurmHICK+mbyCmsp1htJiQomJy2KhE4jnvIrGthT2l7ttPObfW1jC7tK6iivbyYjNpQRyRGEuFlO+1CVge1FemoaTQyKD2PcgGjC+ms105oCKN0O0gKJoyB+aLe7dFagL2BfzT78/PwYFjPMYXUvfbOePdV7qGiqID08neGxw9WC8YpHdTfKyJ2E8DXwM/A2WoG6q4FTpZTneCJQd/hqQlAURfFl3SUEd2oZXQkkAB+h1RtKtLUpPsrYqWO4sxaLBVOnQniKd0mrFWOz6yPTeovJbMLQohasP9G4fP0upawGFvRiLIqH5Fc08NHmIr7fXc4ZwxO5ZEIaWQntnZOGZjNr8qt49ed8AgP8uOGULKZkxqqyEV62v3QLHx34hI3VOzk7ZTrnDpxJesKoPo9jbfFalu1dRomhhFmZszh1wKkMjBzY53Eofc+ViWlPSynvEEJ8hpO1DKSUF/ZKZEqPVBtauHPZVrYcqQVgR3EdP+2t4PVrJxNn689Yl1/FDW+033L7cW8l7/1xGtMHx3kjZAUord7PbavvodBQDMDO6p1sq97FYyc/Rlho343u3lS2idt/uL1tCOz2yu3oW/TMHz+/z2JQvMeVK4S3bP8+0ZuBKJ5xsLKhLRm02lak52ClgbjwICxWyRu/HnLY79OtRSoheFF+zb62ZNDqh5JfKazdz/DQKX0Wx96avQ7zId7b/R6zBs0iKzqrz+JQvMOV4natcw38gbVSyn44s+jEEdBFiYsA2+QEAYQEOm7jbP6D0ncC/Bz/KwoEfqJvfy/O4tD56fDvQe0mpf9xp1N5HrBFCLFGCPH/hBAXCCH6YWGa49ug+DC7OQWgrXfQ2ofg5yf4w4xMu8lrOn/B7JzUvgxT6WRw7DByYkbYtV026HwyYod1sUfvGBEzgthg+1tU12dfr/oQThAuDztt20GIVLSS1QuBVCllnw8sV8NOj664tonV+ytZf7CaKYNiOWlwPGkx7aWuTRYrWw7X8tWOUgL9/Zg5Oomx6dH4dZ7irPSpIxU7+LVkDdurdzMtaRKTkyaTGDu4+x09bHP5Zn4u/JnihmJOSz+NycmTiQ9Va1YdDzw5D+Fq4BQgG6gEVgM/SynXeCJQd6iEoCiK4r5jLn/dwdPAAeBF4AcpZcGxhaYoiqL4EnfmIcQLIUYDpwKPCiGGAnuklL/vtej6ULPJwrZCPVsLa4kNC2RCRgyZ8a7X7gFtfP/WI7VsL9aTEhXM+AExDgvcbD5cw5bDtTS0mBk3IJopA2MIcrMkRHcq65vZUljL/rIGhiSGM25AtFqV7Ggaa6AoF8p3QEwmpE2GKN/rUzGbmtlZtpG8yjxCA0IZm5BDVtI4u22K64vZVrmNUkMpI2JHkJ2QTZjOvb/j7jSaGsmrzGNX1S6SwpLIScghLTzNbpv82ny2VmzFYDKQk5DDqLhR9h3WHjjnVU1V5FXmka/PJysqizHxY4gPUbe2joXL70RCiEggAxgIZAJRwHEzzXXVngpueru9eGtmXChvXDfFrYJun24p5r6P8toe56RH8fLvJ5EcpdWj2Xy4hhvfzKXCtq6An4DFV0/knNHJHvopoLHZzNPf7ePtte1DS383ZQD3zx5FmIcTz3HBYoYNr8APj7S3DT0XLn4BQn1rGG5u8Wpu+vEuLFKbgR4XHMerpz/NYFtSKG8s5+6f72ZbRfs6Vn+b9jcuH365R+P44uAXPLTmobbHY+LG8MwZz5AYlghoyeC6r6+jylgFgJ/w46XfvMS01GnaDh44542mRhZvXczSPUvb2uYMncPdk+8mVNc763+fCNwZZbQabWGcbcAVUsrhUso/9E5Yfava0Mw/vtxl11ZQ1ch2NxamKapp4rFOx9hWqGdXSV3b4w0F1W3JAMAq4cVVB6hp9NyiMfmVBrtkAPDu+iPkl6syBE5V58NPj9u37fsKynd7J54uGI21LN7+elsyAKgyVtktsrO3eq9dMgB4Kvcpihvs5zcci5KGEv698d92bdurtrOnZk/b49yy3LZkAGCVVl7Y+gKNJtuIdQ+c80N1h+ySAcDyfcs5WHfQ5WMojty5ZXTUtZOFEP+RUt527CH1vWazleoGx8VODM1HrwXUUYvFSkOz2aG9saX9GLWNJofnqwwtNLVYcLOgaZc6L2zT3u4YmwKYjWBx/L1g8q3pNi3mZipbah3aa5rbP7Q0WRwXMDKYDLS4uJCPS3FYWzCYHT9cdJzMVttc6/B8RVMFJosJdHjknHeePNfKaDa6fAzFkTtXCN05yYPH6lNJEcH8frr9OGt/P8HwZNdr96dGB3PROPt7oME6P4YmtdcQmpTpOG3jsonppEZ77hI3My6UQfH2x8uIDSHTjVtfJ5TogTDwZPu2kJgelbfuTZHhSVyV5VglZkpy+4CRrKgsQgJC7J4/N/NcUsJcX/u5O8mhycwaNMuuLdg/2G4W84SkCQ77XTXiKqKCbetBeOCcZ0RmOMyNSA9PJyMiw+VjKI7cnofQ5YGE2CSldPxL6AW9Mey0uLaJ/208wjvrDpMaHczCc0YwPSsOfzfG5h+uMvDW2kN8vLmYwQlhLDxnOJMy2yf5NDSZ+H5vBc9/v586o4krJg/gwnGpZMV7dlWs3aV1PP/Dfn7dX8WMIXHcevoQRqQ4rsil2FTshbXPw+4VkDoRzrgPUsd7OyoHVbUFfH7wC97c/yFRughuy76B6WmnEBTc/rvdUr6F/2z+DwdqDzBr0CyuHHGlxxfZOVJ/hKW7l7IifwVZUVncNv42xie1n68WSwtritfw7OZn0Tfr+f2o33N+1vn2Hb4eOOd7q/fyyvZXWFeyjinJU/hjzh8ZFtO3E/n6G4/NQ3Dhhfp1QmhVWd+sLRcZ7NryiJ1ZrZIqQzNhQQGEdtGJW1bXRIvZyoDY3vvU3my2oG8yERWiIyhAlR3olsUEjdUQFAmBId1v70XVdYXo/AKJCE90+nyjqZFGUyOxIbH4CU/eBGhnlVaqm6oJ1YV22Ylb31yPyWoiNqSL4nweOOctlhbqmuuIDIok0N/1pWZPVH2ZEDZLKfvkY5WamKYoiuI+T05M684zHjxWvySlJL/CwKHqRmJCdQxNDHf7SsNilewvb6CoppGEiCCGJIY7LF1ZUNnA7tJ6LFbJ8OQIhvTHdYqVHimqL6KgroAg/yAGRw8mJtjz5cT0NYc4oN+HoaWegZGZZCS7/zmvqPYge2v3Y7QYGRyZxbCE0R6PU/E8V9ZDcLoOQqvW9RCklEs8F1b/9OuBKq5/YwNGkzY949qTMrnjrGFEhbqeFFbuLGX+e5sxWbRTfvc5w7nupMy2pJBXVMtf/reNXaXaqlrpMSE8e+V4JmSoOoPHu91Vu7l55c1tQzqnp0xn0YxFpIZ7bhJdVeVentj6PCsKvwcgXBfO4lP+H+MGnOLyMfZV7OAfuf9iY5k2rycuOI4nT/1/TEzpuzLeSs+4coWg1kFwQWV9M/d+uK0tGQC8/ksB54xOZlqWa5NtjlQ3cvcH29qSAcC/vt7DKUPjyUmPBuCnvRVtyQCgsKaJj3ILVUI4zpksJl7f8brd+P41JWvYXL7ZowlhZ83utmQA0GBq4F9bnuPFqCwiItOOsme7LVV5bckAtPkSS3a+xYjYEYQFqcENvsyV9RB+7ItA+jt9k4kj1Y5jo8vrXB8XXdPYQl2T43yBivr2iWvbi+ocnt9apKex2URoUM86whXfZzAZ2Fqx1aH9QO0Bj75OhbHaoW1X7T4ajLUuJ4SCusMObbtrdlPTVKUSgo9zeQiCEGKoEGK5EGKnECK/9as3g+tP4iOCGDsgyqE9I9b1OQbJkcGk2spctArwE3alq6c7udo4bViCSgbHucigSH6T8RuH9uz4bI++zgAncxZOSZ5CTLjr5VVGxo5waJuRMp3EUM/Nh1B6hztj0l4HFgNm4AzgTdqX1zzhRYXoePS32QxO0IaShgb68/gl2W6N/0+MDOY/v5vQlhQigwN49srxDElon6cwY0gcl05Ia1vg5swRicwao/6jHe/8hB+XDb+M6SnTAQgQAVw/5nrGJYzz6OuMjB/DwuybCPTThnCOih7GbWNuINiNuk7j48Zw9YirCBDaDYiJiRO4bMglBAYGd7On4m3urIeQK6WcKITIk1Jm29p+llK63tvkIb487LS6oZmi2iYignUMjAtFCPcXnSmvM1JaZyQmNJABTq4wGppN7C6pxyolQxLDiQ1TlUxPFA0tDRQ2FKLz05ERkYHO3/NXhpYWI0eqdtBoaiQtMoOoaPdXS2syNrC3djfNlhayIgcRH6E+tPgCTy6Q8wvaAjnLge+BIuCfUsrhngjUHb6cEBRFUXyVJ+ch3AGEArcDDwNnAketdiqECAZ+AoJsr7VcSvlgp20E2hyGWUAjME9KucmNuFxWVNtIg9FCclQQUSGOsxoNRjNF+iZCdP5OP5n3lcKaRkr0TcSGBTI4wXGOgdli5UhNE1arJD02xOlM5Ir6ZqoMzcSFBZIQ4cVL9doj0FwPkakQEt2zY5RuhxYDxA4CZ7Nzm/RQXwSBERDdszINtXVFlBtKiAqOJSkmy+F5s9VMUX0RVqykhac5nRVbVXuIqqYKYkMSiO/Bp2pP2VW1i/qWelLDU0mPSHfcoLke9IWgC4UY53EWVe6mydxISsRAwsIcbxfVNddR1lhGuC6clPDe+fTv0jlvqqLKWEVsUKxXl/ksaSihwdRAUmgSkU46zg0mAyWGEoL9g53/TnyEO9VONwAIIfyA26WU9d3sAtAMnCmlbBBC6IDVQogvpZRrO2xzHjDU9jUVrZ9iqqtxuaLFbOXbnaXc//F2ahtNjE2P4vFLc+zu7x+oaGDRJzv4eX8lYYH+/N/5I/ntuDTCgvp2DYG1Byq5/+MdHKhoICEiiAdnj+Lc0ckEBGjdPZX1zbz6y0Fe+Tkfi1Vy+aQB3H7WUFKj2zue1x+s4s5lWymsaSI9JoQnLhvr8tBXjzG3wJ7PYcWfoakGUifARf+BpDGuH8NQDVvfhR//qb2JZcyAcx6FtA4VUsp2wKe3Q9FGrUDarCdh5AUQ4HoZgx3F6/jrukfZX3eQ+JB4Hpr0F04eeDZ+/trvvrKpkrd3vs0bO9/AKq1cPORibsq5ye6NMPfIT9y/7lGKDMWkhaXyyJT7mJRxuus/qwc0GBv4+vDXPLXpKfTNekbEjuDeKfcyMWli+0aVe+GLeyD/ewgMh5mPQPblEKT1fTU1VfP5gU95YtuLGEwGpiZO4P6JdzEosb3Y8d6avSz6dRF5lXlEBUVx/9T7OSvjLI+WjnDpnJflcv/P91NkKCItLI1HTn6EScldfvjtFSaLie+PfM/Dax9G36xndNxo/j7j7wyLba+pdFB/kH+u/ye/Fv9KaEAod026i9lZs31y3QZ3RhlNEkLkoa2HkCeE2CqEmHi0faSmwfZQZ/vqfI/qIuBN27ZrgWghhEc/cuwpq2f+e5vbyk9vLdTz14+3U9+kPW42W3juu338vL8SAEOLhfs/2u7WegiecKjKwF8+yONAhXbKKuqbuXPZVvKK2+NYk1/F4lUHMFkkVgnvbzjC1ztK254/Ut3IjW/lUlijDYEtrGnixrc2cqS6j8s5l++E5ddqyQCgeBOsuAuMjsNmu1S4Ab65X0sGAId/hZ+fbH/cXA9f3K0lA9Be68PrtVW4XFSlP8Tdaxax31ZHv7Kpkjt+uZ+DldvbttlYupFXt7+K2WrGKq18sO8Dvj/SPla/qHI3C375P4oM2roDRYZiFvxyP4WV9utj9La86jweWvMQeltJ7N3Vu3ly45Pt6yGYm+HHJ7RkANDSACvugJItbcfYVbGVh3KfxGDSSlyvK9/Ei3mv0Nys/U02tDTwj3X/IK9SWwhK36znnp/uYV/NPo/+LN2e8/oiFvywgCJDkfbYoD0urC/0aBzd2Ve7j7t/vLvtnO+o2sEjax+hvkX7G22xtPDytpf5tfhXABrNjTy89mF2Vu3s0zhd5c4oo9eAW6SUmVLKTOBWtJFHRyWE8BdCbAHKgW+llOs6bZIGHOnwuNDW5jEFlQY6d5VsPFRDmW18f3VDC192eFNtlV/Zt4vKFFY3crjTG3eLxcrBDnF8t6vMYb+PNxfRYtbWQSiqbXJYd6GuyUxhTR8nhOp8HE76kbVQX+LeMTrb/y3U2v7T15fCoV/sn5cSqlwfm19WX8SRBvs3EZPVxBF9+yJDq46scthvRf6KtnUGShoK294QWtW11FHcx29Oh+sOIzt93sqrzKO0wfa3baiA3Z867li1v+3bgrojDk+vLP6FaltSqWyqJLfDpDMAieRQ/SGH/Y5Ft+fcUOL8nHtwMSBXODvnmys2U9FYAUC1sZqVh1Y67FdQV9AX4bnNnYRQL6X8ufWBlHI10O1tIymlRUo5DkgHpgghOt8zcDYMx6GnWwhxoxBioxBiY0VFhRthQ3y446VsUmQQkcHaLYHwoACGJzneq08I79vRO9FhgYQ7uUXVMY4xaY5zHSYMjEHnr/0qY0J1BHQq2e3vJ4gJ6+NKkGEJjm0RKRDsGH/Xx3BymytuaPsxgiLB2WSpLqqAOhMRFO10zeG4DqWaR8WNcnh+bPxYdH7aCJ+ooNi2IZat/IU/McFdVPnsJc6qiiaGJhIRaPvbDoqEhJGOO3Y4X3FBjjPeh0RmEhao3V4NCwwjKTTJYZu4YM/ekuz+nEd1cc77dsZ+rJPfccdzHqYLY3D0YIdtfHXtZ3cSwnohxEtCiNOFEKcJIV4AVgkhJgghui17LaWsBVYB53Z6qhDo2BOYDjikeSnly1LKSVLKSQkJTt5sjmJkSiSXT2rvyAnwE/zj4mwSI7XO1ogQHX89fyTBuvbTccbwBLLT+3ZW5ejUKO6bNYKOI1X/MH0gIzv0dZw5IpHB8e1vYAnhQVw+aUDb8NZB8eH89Xz7//T/N2sEgz285kK3kkbD+N+3P/YLgAuegQg31o9OHQ9Zp7c/DgiGsx5sX4w9IgkueFo7dqtxV7nVTzEgYRR/m/BnRIfPJdcPu4KsuPbBc6emn8qgyEFtj+OC47hk6CVt5zwzbgQLx/7J7rh3Zd9EppM3td40NHoo52a2//cKEAHcNfEuhsbaFp4JjtT6YHQdSk0PmQkp49oejoofzWnJ7V14Qf5B3D3+diJtv7eEkAQenP6g3ZvxRYMvYniMZwcbdnvOIzNZOHmh3T53TbyLzMhMj8bRnWGxw7hkyCVtjwNEAA9Me4CEUO09KiIwgr9M/gvB/u0DO05JO8VpwvMF7gw7/eEoT0sp5ZlO9kkATFLKWiFECPAN8LiUckWHbc4H5qONMpoKPCulPGoVrJ4MO61tbGF3aT01jS1kxoUxLCnCYfGbfWX15Fc2EB6kY0RyBHF9fIUAYGg2sa1Qz+HqRhIjghmTGklCpP0ooeLaJvaWadVOhyZFOMyGbmoxs7esgeLaJlKiQhie7FgxtU801mh9CY1VEJsFiSPBz821GaoPQmkeNNdB/DAY0OlPw2qB8l1QcxBCYiFxFIS69ymxpcXAgcrtFNYdIT40gaGxowgPt//QUWooZV/NPqzSyuDowQ4jRYxNteyv2kFJQwnJYSkMjR9NcE9HVR2Dw/rD7K3dS62xlgGRAxgbN5bgzhPCKvZonctBkdr56vSzVtceZl/NbhpaGsiMGsTgTtVOLVYL+2v3c6T+CNFB0QyNGUpUkBtXfi7q9pybjeyv3U+JoYTk0GSGxgwlOKDvR9Tpm/Xsq9lHbXMtGREZDI4ejH+nv/P82nwO6g8SpgtjWOwwp1cWfaHP1kPo4sVzgDcAf7SrkWVSyr8LIW4GkFK+aBt2+hzalUMjcK2U8qjv9moegqIoivs8Ng9BCJEE/ANIlVKeJ4QYBUyXUr7a1T5Sym2AQzF1KeWLHb6XaB3UPqGuyURQgB9BOrXKmM8wGcHSfPT+h6Za7VZIQC9e1bUYQFohqIv1J6wWbRRVcGTXV0LNDSAEBDpfLc9kNdFkaiI8MLzr1c6M9dptsmNZ2c2oB/8g0B3/5SQMJgMC4ZPDPH2NO/cRlqCNKrrf9ngvsBToMiH0J2V1Rr7IK+HttYdIjwll/hlDmJQZ06PSE4qHSAmH12pDTWsLYOK1MPoSiOwwKrn2MGxbps1XSBwNJ98BaUcdDe0+UxPk/wg//UsbunnyHdq995AOfUwVe2D9f+HAdzD4TJj8R0jsUOTNWKeNkFr9NPjp4LSFMOh0CGx/k9pTvYe3dr7F5vLNnJVxFpcOvZSBUR0mjhmqYM8X2lrEIXFw6kLIPBncKV9RVwo7P4KNr2qL3Z9yF2RMh+Pw77yhpYGfi37mtbzX0Pnr+GP2H5meOt0rt5X6C3f6EDZIKSd3XCpTCLHFNoKoT3n6lpGUkv98v49/f9s+ljrQ348Pb5nhdFSP0kdKtsErZ4FtqCEAp98Hp92jvYGZW7R5CJuWtD8fFAE3fAcJHuzkzP8R3rzQvu2yN2H0Rdr3DRXw5kX28x/iR8C8z9pH8Oz6DJZebX+M338Mg88AtJmuV31xFRVN7SPopqdM59+n/5vwQNuAgNw34bPb2vcXfnDtV5Dh4jxOKbWk9sOj7W3+gXD9t5A6zrVj9CPfHf6OO364w67t5bNfZnrqdO8E5AO6u2XkzigjgxAiDtuQUCHENKBvZ271krK6Zl5ZfdCurcViZVeJG5OoFM8r22GfDADWPAd1tkFo+iOwpVPB3eZ6rZPZk7Z/6Ni2/iVtkXiA6gOOk+Eqd7fPh7CYtauHzvKWt32br8+3SwagLYBzpN42L8BYB2uetd9fWuHgT67/HPUlsOZ5+zZLi3aejzNmq5n3dr3n0P5F/hdeiKb/cOeW0Z3Ap8BgW6G7BGBOr0TVx3T+gvDAAIfFaVQ/gpc56w8Iimi/ReKvg4AQbcat3X4eviUQ6mRESGi89gm9qzihvXyG8ANn5aM7HDfI3/EY/sK/vZqpnz8ERzseo6v+DGf8dBAUDsbaTnEef7dQ/ISf0zkJ0c7OodLGnSuEwWh1h2YAXwP7cC+h+Ky48CDunWW/qEdSZBDZqWp1J69KGec48ew3D7XfhokaAGf+1f75hJGQ7Ea9JFeMvNB+7L6fP0y9qb3jOG4I5Fxhv0/2ZRBnq2fj5wdTb7SfL6ELgdEXtz3Mis5iSrL9kNqrR17NwAhbH0JgGJx2r/29/uBoGORG9fnwBO38dRSRAqljXT9GP+En/LhyxJV28yWC/YM5J/McL0bl+9zpQ9gmpcwRQpyMNtroSeD/pJQeLUTnit4YdtrYYmbL4Vp+PVBFUmQQ0wfHMSTRjU9fSu+o3AsHf4a6Ihh0GqRNaivEBmijZY6s10pYxGRpnaxxjtVKj1nJFq0vwdyiTZZLm2A/kqiuRCvNUbxZS2QZ0+07v61WrZ5T/irtymbQaQ737UsaSsgty2VvzV7GJo5lXMI44kI6XFmYW6AoVztGSDQMOlWbAOiOFgMUbtRuNUWkaAnFk/0tPsQqrWyv3M7akrXo/HRMTZnqsxPC+oon10PYLKUcL4R4DMiTUr7bsYO5L6l5CIqiKO7zZKdykRDiJeBy4AshRJCb+yuKoig+zJ0+gMvRZhM/YStFkQLc3TthKYrmoP4g60vWU9ZYxuTkyYxNGGs/waiuBA79qt2uicmEgSc53IrZW7OXDSUbqG2pZVrKNLLjsz1aux+Air1aee6SbZCSo63dkNBeE99itbCjagdrS9YS4BfAtJRpDrcvWm8Z7avdR05CjsMto0ZTIxvLNrK+ZD0RgRFMTp7MhKROZcSq86FgtTbCaeAMGDC15wsTdcWohyPrtPPem7fp+kB359wTmsxNbK3YyvqS9SSGJjIleQpZ0b55vnq1dEVvUbeMTgyH6w5z/dfXU9rYXpr8n6f8k/Ozzm/f6Kcn4PuH2x/HDYEr3tbqJgH7avYx76t51LVoQ4gFgufPep5T0j24FHhDOXy+EHZ90t424nw4/2mI0DrAN5Vt4vqvr8cstZFswf7BvH7u64yJ1zrAa4w13P3j3awrba8OP2/0PG4ff3vbSKNvC77lrh/vaiu3HBkYyXNnPcf4RNtdW30RvDNHqx/VauajMP1Wz048W/sifHVP++OEkXD1cojy3ZXAnHHlnHvClwe/5C8//aXtcVJoEq/OfNV+0mEf8eQtI0XpU7uqdtklA4Cncp+iqqlKe1C2A1Y/Zb9T1X6tGJ5NblluWzIArXb/4i2L2xaA8YjyXfbJAGD3521zE6SUvLf7vbZkAGC0GPmm4Ju2xwdqD9i9MQG8tfMtDtcfBqDWWMvrO163q71f11LHxtIOH4zKttsnA4BV/9Bmc3tK7WH7BAxQsUtb5rSf6e6ce0K1sZqncu3/Rssay9hZ3f8XyFGUPtVsbXZoM5gMmKy2CWEWE5ibHHc0G9u+bTQ5LgxUZ6rDbDU7tPeYyei83azFb5EWaow1Dk/XNte2fd9scfxZLdKCyTb5zWQ10dB5vgXQYOrQZm5xeB5TkzYxzlNcOOf9RXfn3BNMFpPTDx/OXtsXqISg+KxhMcMI9LO/13/NqGvaF2iJGwpjOs2NDIrQahrZTEia4FAk7ppR13i2XHPCcMehm/FD29oC/AK4cuSVDrudN+i8tu+zorJIDLFf2Gd6yvS2ks8JoQnMGWb/s/oJPyYnTW5vSByhlbTuaNxVEO3BWzlRA2Dc7+3bgiK0Mtr9THfn3BMSQxO5ZtQ1dm06Px3DYoZ1sYd3qT4ExWdJKdlcvpn/5v2XwvpCLht2GecOOpfE0A7/ict2QN7/YNenEDsYps+HrNPanjZZTeSW5vLStpeoaa7h9yN/z5kDziQmxMMraxVuhI2vax3LA6bBpOtgQPubdWuhtVfzXiXQP9BpobXW4nabyjfxm4zfcOmwSxkY2X6fubC+kB+O/MAH+z4gQhfBvNHzmJE2g5CADpPmijZpBfTK8iBnLoy9EmIyPPuz1h6GrUth23u9V1Cwj3R3zj2hvLGcbwq+YdmeZaRFpPHH7D8yPnG8VwpnenU9hN6iEsKJpdnSTLOlmcjAo8wcryvWPh0HOV8Zzmg2YrKa2peT7A1mk9bBHJ4IAc47JbsrxWyymmg0NRIRGNFl+euqpioC/QKJ6KpshbkFTI2eH13UWV+UHO8DrpxzT6hrqSPIP8hpmZK+4rH1EBTFW1z6TxSZetSngwOCCaaXa/YE6CDayRrPHThbv7kjnZ+u29tZ3Q6LDAhsr6PUm7ywIlxvcOWce8JRP9D4CJUQFOea66FsJzSUaXXzE0f2zZtMZ4ZK7bZQc53tvvyI7vfppKBqN3tqD2C0GBkSlcXopB5MrtcXaSN4rBbtXMR4fshgQ0sD+2v3U9FUQVp4GkOihzjMlzikP0S+Pp8g/yCGxgxtW7tXUTxBJQTFUXMD/PKMVjsftDHsv30Jci7v24VU6kthxR2w50vtsS4ErvoQMme4fIg9ZVt4YP1jbcP8InQRPH3aE0xJc/0YVO6D93+n1VUCCE+G33/ofh2ho2g0NfL6jtd5edvLgDZf4tGTH2V21uy2e807Kndw47c3tg2jzY7P5l+n/ou0iKNflSiKq9QoI8VRxZ72ZADawiqf36ktZN+Xije3JwPQhlB+fa9279pFm6t32o35rjfV8/rOt2horHY9jn1ftycDgIZS2PSmdl485ID+QFsyAG2+xCNrH2lbD6HF0sIrea/YzanIq8wjtzzXYzEoirpCUBw1Vjq2tTS49UbsEQYncVTs1m5nuXj/urC+yKHtgP4AdcYawp2tc+BM8VYnB16vjcn30G00Z/MUGs2NbQmg0dTIrmrHhX8O1R3yyOt7m8lkorCwEKOx/81n8EXBwcGkp6ej07k341olBMVR9EBt5Ii52b4tso9LE8QNdmwbcUH7egguGB030qHttPRTSXZnaOGwcyFvmX1b9hUe7VNJC08j0C+QFmv75LK0sDSSQ5MBiAqK4rxB5/FK3it2+42NPz7WMigsLCQiIoLMzEy1jvkxklJSVVVFYWEhgwYNcmtfdctIcRQ/DK54ByKSbY+Hw5zX2+ry9JmUsXDBM+2rgg06XVtP2Y1hjuNjRnBz9k0E+2sjjE5PP41LBs3GL8CNz0KDToGT/6ytYyD8YMIfYORs1/d35SWiBvHMmc+QEKJ1EmdFZfGv0/5FfGg8AEIILh16KedknoNAEOQfxB0T7mBs4vGREIxGI3FxcSoZeIAQgri4uB5dbal5CErX6kqgqVrrRA3zbAVIt9QUaP0HkekQ7P48ArOpmX3VO2mxmhgUkUmkG1cYbSxmLQ6s7VdQvaCssQx9s56EkASnS0AazUaKGooI9AskLSKtV8fN96Vdu3YxcqTj1ZzSc87OqZqHoPRcZIr9ql/eEpN5TLsH6IIY2ZOhph35B0D8kGM7hguSQpPaS3M4ERwQzOBoJ7fSFK+ZNWsW7777LtHR0d4O5ZiphKB4V2ONVgohMBRis+yXpXSVoRL0hdpM5dhBjkNjzSaoydf6RGIyIbh3JgiVGkqpaqoiNiSWlDAfSKRKn/jiiy+63cZsNWOymvDDj0D/QJ+9NXZ8XG8q/VP5bnj7Enj5VFg8A9Y8D8a67vfrqGQrvHYuvHwavHgSbH5bu73UylAFP/5TO/5Lp8D7V0Hlfs/+HMCa4jXMXTGXuZ/PZe6Kufxa9KvHX0PpOYPBwPnnn8/YsWMZM2YMS5cuJTMzk3vuuYcpU6YwZcoU9u/X/i4qKiq49NJLmTx5MpMnT+aXX34BoKGhgWuvvZbs7GxycnL44IMPAMjMzKSyUhsR9/bbbzNlyhTGjRvHTTfdhMViwdBs4IqrryAnO4fsnGwe/dejWKwW75yIbqiEoHiHyajV6i/epD22tMC3f4PiLa4fo0kPK+6Eqn22YzbCp/O1GdatCtfDz09Aa7nrgp9g46vajGMPOVJ/hDtX3UmVUVunodpYzZ0/3snhOg+uQ6Ack6+++orU1FS2bt3K9u3bOffccwGIjIxk/fr1zJ8/nzvuuAOABQsW8Oc//5kNGzbwwQcfcMMNNwDw8MMPExUVRV5eHtu2bePMM8+0e41du3axdOlSfvnlF7Zs2YK/vz9vvf0Wq9atori4mI9//piPfvqIsy49iyZnJcR9gEoIinc0VsK+bxzbq/NdP4ahHIqcDC6o6XAMZwlm16fQ5Djuv6fKDGX26xKgFbErNZR2sYfS17Kzs1m5ciX33HMPP//8M1FRWu2iK6+8su3fNWvWALBy5Urmz5/PuHHjuPDCC6mrq6O+vp6VK1dy6623th0zJsa+0/+7774jNzeXyZMnM27cOL777jsOHDhAbFoshYcK+ce9/2D1d6sJjwi3G17sS1QfguIdwVGQPFZbC7kjdzqxg6MgOhNqC+zbIzocI95J3fn0Ke1DWT0gJjgGnZ+ufeEetDUQPL02r9Jzw4YNIzc3ly+++IL77ruPmTNnAtjdy2/93mq1smbNGkJCQuyOIaU86r1/KSV/+MMfeOyxx9raLFYLh+sP8+EPH/LLD7/w3mvv8dUnX/Hmkjc9+eN5jLpCULwjKALOeVR7U2815lJIHef6McIT4cJntRpHrab+CZLGtD/OmApZHS7twxK0OQUeHDY6MHIg90+9v20IqJ/w4/6p93u8rr7Sc8XFxYSGhnL11VezcOFCNm3SblUuXbq07d/p06cDMHPmTJ577rm2fbds2eK0vabG/irzrLPOYvny5ZSXlwNQXV1N4ZFC/Bu1gRJnX3A2t917G3u37yXE3z7Z+Ap1haB4T/okuHGV1skbHAHxIyE02r1jDDoVbvxJq7MUEqNVQ+04iigqHS59RSt5YW7SVlnzcKXSAL8ALhx8IaPjR1NmKCMpNIms6CwC/NR/L1+Rl5fH3XffjZ+fHzqdjsWLFzNnzhyam5uZOnUqVquV9957D4Bnn32WW2+9lZycHMxmM6eeeiovvvgif/3rX7n11lsZM2YM/v7+PPjgg1xyySVtrzFq1CgeeeQRZs6cidVqRafT8fzzzxMSEsKN196IxaL1Wz3+2OME+Pvm34aamKYoitd5Y2JaZmYmGzduJD4+vk9ft6/0ZGJar94yEkIMEEL8IITYJYTYIYRY4GSb04UQeiHEFtvXA70Z0/FC36yn1lh7bAcxVGnzALytoVwrWNeV5nptmy4+vFillcrGShpNjb0UYN8xNtdRWVuAyeTdUSgGk4Gqpir64wdGped6+7rFDNwlpdwkhIgAcoUQ30opd3ba7mcppWeLwxynGk2N/Fz4M89teQ6T1cQN2Tdw9sCz3VvxqbEGdn8GPz+pTQQ7434YOtOjHa0u0RfC5ncg9zWtLMVZD0Dmye2T06wWOPQLrPw71B2BCfNg/NUQPaDtEEfqj7B091JW5K9gcPRg5o+bz/hjnZXsJTtLNrA477/k1ezltOQpzBt5NYMSc/o0BovVwsayjTy76VlKDCXMGTaHi4dcTEr48TfRrqCgwNsh+JxeTQhSyhKgxPZ9vRBiF5AGdE4Iios2lW9i4U8L2x4/tOYhQgNCmZU1y/WD5P8An97W/nj5dXDV/7Sk0FesVtjwKqz+t/a4vhTevhiuXwlpE7S20m3w1sXtcwh+/KfWD3DWIvDzo9nczAtbXmBF/goAqkqruPHbG3lv9nsMie79MhOeVFi5i5t/vIuaZu2K7cOCLzlQd5gXTn+ayNYig31gd/Vubv72ZsxSO+eLty6m2dLMggkLjpu6SUrX+uw3LITIBMYD65w8PV0IsVUI8aUQwnPLUB2Hvi341qFt6Z6lmC1m1w5gsWhvxJ1t/+AYI3NTfQlssC/ljNWiLZfZqnxXezJoteEVqC8GoLSxlC8O2pcNMFqM5Ne6MZfBRxTo89uSQaut1TsorCvo0zj21+5vSwat3tv9HuWN5X0ah+IdfZIQhBDhwAfAHVLKzrUJNgEDpZRjgf8AH3dxjBuFEBuFEBsrKip6NV5flhjqWKkzOSwZPz8Xf5V+fhDlZMnFyD5ehjEgGJwtUBMY3v69LtTx+dA48NeGjAb6BRIW4LhofUiAbw7pOxpnwxADRADBAcF9G4eTcxcTFEOgnxfW01b6XK8nBCGEDi0ZvCOl/LDz81LKOillg+37LwCdEMKh219K+bKUcpKUclJCwom7sPiZGWcSrmt/09T56bhyxJWuX84LAZP/aD8OPygCRl7k4Ui7ERYHMx+xb4sZZD8PIXUcxHaq7DnzUQjXfv8p4SncOelOu6fHxI1heMxwz8fbywbHDef05Gl2bX8c8TsGxPbtzzIqbhQDI+yH5S6cvJDYEBdXl1P6tV4ddiq0aX1vANVSyju62CYZKJNSSiHEFGA52hVDl4Gd6MNO99bsZVvFNsxWMzkJOYyMHele9UQptaJwRRtBBMCAyR5dMN5lJqO2bnLRJi1BpE+BuCz7barz4cgGMFRofQupE0DX/qm50dTI9srt7KreRVJoEtkJ2aSF989F58uq95NXmceR+kKGRg8hOyGHqL6+cgMO1x1mW+U2qpqqGBM/hjFxYwjqpfUfWvW39RCWLFnCzJkzSU1N9XYoXerJsNPeTggnAz8DeYDV1vx/QAaAlPJFIcR84E9oI5KagDullEctFXmiJwRFOd70t4Rw+umn88QTTzBpUpfvrV7ncwvkSClXA0f96CqlfA547mjbKIqidPTx5iL+9fUeimubSI0O4e5zhvPb8cd2NWUwGLj88sspLCzEYrHwt7/9jSFDhnDnnXfS0NBAfHw8S5Ys4ZdffmHjxo1cddVVhISEsGbNGn799VcWLlyI2Wxm8uTJLF68mKCgIO69914+/fRTAgICmDlzJk888QSfffYZjzzyCC0tLcTFxfHOO++QlNT1okh9Sc1U7m+sVq1k9O4V2oIvIy7QSkB4cMH3PlNfCgWrYe/X2tyCoedotYeUE447Vwgfby7ivg/zaDK1lzAP0fnz2CXZx5QUPvjgA7766iv++9//AqDX6znvvPP45JNPSEhIYOnSpXz99de89tprdlcIRqORoUOH8t133zFs2DCuueYaJkyYwDXXXMP06dPZvXs3Qghqa2uJjo6mpqaG6OhohBC88sor7Nq1iyeffLLHcXfF564QlF5QvAleP09bPwBg3WK45lOtpk9/s/MT+PIv7Y9zl8DvlmkJTlG68K+v99glA4Amk4V/fb3nmBJCdnY2Cxcu5J577mH27NnExMSwfft2zj77bAAsFgspKY4T9Pbs2cOgQYMYNkyrrPuHP/yB559/nvnz5xMcHMwNN9zA+eefz+zZ2tzbwsJCrrjiCkpKSmhpaWHQoEE9jtnT1EyT/mbnJ+3JALQO4rWLPbrgS5+oPgirn7Jva6zSJqMpylEU1zov69FVu6taS2RnZ2dz33338cEHHzB69Gi2bNnCli1byMvL45tvHNfw6OouS0BAAOvXr+fSSy/l448/bluU57bbbmP+/Pnk5eXx0ksvYTQajyluT1IJob9pcVKvp6Wxyzo/PstqAUuzY7urE+yUE1ZqtPN5Jl21u6pziex169ZRUVHRtnCOyWRixw5t4mRERAT19Vr9rREjRlBQUNC2BOdbb73FaaedRkNDA3q9nlmzZvH000+3ldHW6/WkpWlXMm+88cYxxexp6pZRfzPmYsh91T4BTLsZfLScbpfih8DkG7VyFK10IZA8put9FAW4+5zhTvsQ7j7n2OZsOCuRHRAQwO23345er8dsNnPHHXcwevRo5s2bx80339zWqfz6669z2WWXtXUq33zzzVRXV3PRRRdhNBqRUvLUU9oV8aJFi7jssstIS0tj2rRpHDx48Jji9iTVqdzfmFu0gm9rntfq+ky7Ves/CArvfl9fU5WvLaOZt1Qrbjf5esg63dtRKV7g7rDT3hhldLxRncongoBAGHwGDDwZhAT/fji6qFVcFsTdDOOu0kpZBOi8HZHST/x2fJpKAL1AJYT+6nh68wzu47LbiqI4pRKCG8rqjOwqqaPOaGJIQjgjkiPx83OjZERfaa6H0u3aegNRadoawx2XlfQVViuU74SKPVpSSMqGyOOv7r7LGmugbLs2PyMmU+tP0fW/Qn1K/6USgotK9E38eekW1uZXA6DzF7w+bwonD/Wx5fdMzbDuZfj+7+1tZ9wPM263qwHkEwpWwzuXgMWkPc6YDpe+6rwa6/HOWKd1sK97sb1t9tMw4Q9ahVpF6QPqL81F24vq2pIBgMkiefjznegbW46ylxdU7YcfOlURXfWY1u5Lmmrh6/vakwHA4TVasbsTUcVu+2QA2vmp6X9rOyj9l0oILqoxOL7xF1QaaGjxsQlhxlqQVvs2adXegH1JS4NWybSzxqq+j8UXOPv9mJrAeJS1phXFw1RCcNHgRMeFWC4cm0pieO+WBXZbdAaEdVovIiweYgY6395bwpMg+zLH9oT+t5aBR8QOclwQKG6o3frRSv/ywAMPsHLlSrf3W7VqVVuZi76mEoKLxqRG8eyV44gLC0QIOD87mVvOGIwuwMdOYfQAuPJ9SM7WHieOgbnv+94bi78OTloAYy7VFu0Ji4dLXoGUsd6OzDvih2p1nOJsa0GnT4U5r2nnRfFZUkqsVqvT5/7+97/zm9/8ptdjMJs9N7tfdSq7KEjnz4Vj05iSGYvRZCUlKpggnb+3w3IufRJc8xk0VmvLVIbGeDsi5+IGw0UvwBl/1Tq8I313sZE+MegUuO5raNJrq8L54sgwX7FtGXz3d9tIunQ46wHIubzHh7vnnnsYOHAgt9xyC6DNJo6IiMBqtbJs2TKam5u5+OKLeeihhygoKOC8887jjDPOYM2aNXz88cc8+OCDbNy4ESEE1113HX/+85+ZN28es2fPZs6cOWzYsIEFCxZgMBgICgriu+++Q6fT8ac//YmNGzcSEBDAv//9b8444wy7uKqrq7nuuuvIz88nNDSUl19+mZycHBYtWkRxcTEFBQXEx8fz7rvvHtPpbKUSgpuSo/rJMMDQGN9NBB3pgh1XSTuRhcWrq4LubFsGn92u9bEA6I9oj6HHSWHu3LnccccdbQlh2bJl3HvvvaxevZr169cjpeTCCy/kp59+IiMjgz179vD666/zwgsvkJubS1FREdu3bwegtrbW7tgtLS1cccUVLF26lMmTJ1NXV0dISAjPPPMMoJXM2L17NzNnzmTv3r12+z744IOMHz+ejz/+mO+//55rrrmmrSZSbm4uq1evJiTEc+9JKiEoigsMjVUcrtmHRVrIiB5CZHgPFjRp0kP1ARB+2tVRkJqQ1yPf/b09GbQyNWntPUwI48ePp7y8nOLiYioqKoiJiWHbtm188803jB8/HoCGhgb27dtHRkYGAwcOZNo0bQ3srKws8vPzue222zj//POZOXOm3bH37NlDSkoKkydPBiAyUrvyW716NbfddhugFcgbOHCgQ0JYvXo1H3zwAQBnnnkmVVVV6PV6AC688EKPJgNQCUFRulVatZcnN/+Hr4pWATA1YTwPTr6XAQmjXD9I9UH47A44qB2DUb+Fcx7Vbnco7tEXutfuojlz5rB8+XJKS0uZO3cuBQUF3Hfffdx000122xUUFBAW1j7IJCYmhq1bt/L111/z/PPPs2zZMl577bW256WUTtc8d6WOnLNtWo/VMQZP8bEeUUXxPWtL1rUlA4B1FZv5suAr9w6y4+P2ZACw82PY/70HojsBdZVEjzG5zp07l/fff5/ly5czZ84czjnnHF577TUaGhoAKCoqory83GG/yspKrFYrl156KQ8//DCbNm2ye37EiBEUFxezYcMGAOrr6zGbzZx66qm88847AOzdu5fDhw8zfLj9KLuO26xatYr4+Pi2K4zeoK4QFKUba8s3ObR9X7aeeS2NBAaGOtmjE4sZ9nzu2J7/A0y8xgMRnmDOesC+DwG0Eh9nPXBMhx09ejT19fWkpaWRkpJCSkoKu3btYvr06QCEh4fz9ttv4+9vP5ikqKiIa6+9tm200WOPPWb3fGBgIEuXLuW2226jqamJkJAQVq5cyS233MLNN99MdnY2AQEBLFmyhKAg+2HsixYt4tprryUnJ4fQ0NBeXz9Blb9WlG4s2/YqD29+2q7txuG/47Zp97l+kB8es1/7AWDWkzDlhmMP8DjgbvlrT48yOh6p8teK0gtOSp3BlCM/sL5yKwDDIrOYPeh89w6SfRns/hzK8rTHA0+CIb0/Rv24lXO5SgC9QCUERelGWvxInjzlcQ7aRhkNih5CXLSbM7/jh8DvP4TKfVqxuvhhEBrXOwErSg+phKAoLoiOTGN85DFWYQ1P1L4UxUepUUaKoigKoBKCoiiKYqMSgqIoigKohKAoiuJUcXExc+bMcXu/WbNmOdQz6qynpbF7m5qHoCiK17k9D8GLzGYzAQG+Px6nJ/MQ1BWCoij9zuf5nzNz+Uxy3shh5vKZfJ7vZCa4G+655x5eeOGFtseLFi3iySefZMyYMQAsWbKEyy67jAsuuICZM2fS2NjI5ZdfTk5ODldccQVTp06l9UNqZmYmlZWVFBQUMHLkSP74xz8yevRoZs6cSVOTNrt63rx5LF++HIANGzYwY8YMxo4dy5QpU6ivr6egoIBTTjmFCRMmMGHCBH799ddj+vlcpRKCoij9yuf5n7Po10WUGEqQSEoMJSz6ddExJYW5c+eydOnStsfLli1rq07aas2aNbzxxht8//33vPDCC20VUf/2t7+Rm5vr9Lj79u3j1ltvZceOHURHR7dVLm3VWhr7mWeeYevWraxcuZKQkBASExP59ttv2bRpE0uXLuX222/v8c/mDt+/7lEURengmU3PYLQY7dqMFiPPbHqG87PcnEFu46z8dUZGht02Z599NrGxsYBWlnrBggUAjBkzhpycHKfHHTRoEOPGjQNg4sSJFBQU2D3fVWlsg8HA/Pnz2bJlC/7+/g5lsXtLryYEIcQA4E0gGbACL0spn+m0jQCeAWYBjcA8KaVjNTFFURSg1FDqVrurOpe/7qxjuWlX+147Fqvz9/dvu2XU8TjOSmM/9dRTJCUlsXXrVqxWK8HBwa7+GMekt28ZmYG7pJQjgWnArUKIzkXkzwOG2r5uBBb3ckzHv5oCWLsY3r4U1rwA1QXejkhRPCY5LNmtdld1Ln99NCeffDLLli0DYOfOneTl5fXoNbsqja3X60lJScHPz4+33noLi8XSo+O7q1cTgpSypPXTvpSyHtgFdJ7/fxHwptSsBaKFECm9GddxralWW4jlq3th/0r4+j749HZorPF2ZIriEQsmLCDY3/4Tc7B/MAsmLDim43Yuf300t9xyCxUVFeTk5PD444+Tk5NDVFSU26/ZsTT22LFjOfvsszEajdxyyy288cYbTJs2jb179/bKYjjO9NmwUyFEJvATMEZKWdehfQXwTynlatvj74B7pJRdjitVw06PonAjvHKWY/v1K2HAZMd2RfEB7g47/Tz/c57Z9AylhlKSw5JZMGFBj/sPesJisWAymQgODubAgQOcddZZ7N27l8DAwD6LoTs+W/5aCBEOfADc0TEZtD7tZBeHLCWEuBHtlpJDZ4/SQZcJvv/NN1GUrpyfdX6fJoDOGhsbOeOMMzCZTEgpWbx4sU8lg57q9YQghNChJYN3pJQfOtmkEBjQ4XE6UNx5Iynly8DLoF0h9EKox4e4IZB5KhT81N6WMUNrVxTFIyIiIjge71L09igjAbwK7JJS/ruLzT4F5gsh3gemAnopZUlvxnVcC42BC5/VFmPZ+xUMnQkjZ0NorLcjUxTFx/X2FcJJwO+BPCHEFlvb/wEZAFLKF4Ev0Iac7kcbdnptL8d0/IsdBDPma1+K0k90NQRTcV9P+4Z7NSHYOoqP+huWWuS39mYciqL4tuDgYKqqqoiLi1NJ4RhJKamqqurR3AU1U1lRFK9LT0+nsLCQiooKb4dyXAgODiY9Pd3t/VRCUBTF63Q6HYMGDfJ2GCc8VdxOURRFAVRCUBRFUWxUQlAURVGAfrpimhCiAjjkxRDigUovvr47+kusKk7P6i9xQv+J9XiIc6CUMqGrHftlQvA2IcTGo9UD8SX9JVYVp2f1lzih/8R6IsSpbhkpiqIogEoIiqIoio1KCD3zsrcDcEN/iVXF6Vn9JU7oP7Ee93GqPgRFURQFUFcIiqIoio1KCN0QQvgLITbbVnbr/NzpQgi9EGKL7esBL8VYIITIs8XgUKRdaJ4VQuwXQmwTQkzwRpy2WLqL1VfOabQQYrkQYrcQYpcQYnqn533inLoQp9fPpxBieIfX3yKEqBNC3NFpG185n67E6vVzaovjz0KIHUKI7UKI94QQwZ2ed/+cSinV11G+gDuBd4EVTp473Vm7F2IsAOKP8vws4Eu0yrPTgHU+HKuvnNM3gBts3wcC0b54Tl2I0yfOZ4d4/IFStPHwPnc+XYzV6+cUbW36g0CI7fEyYN6xnlN1hXAUQoh04HzgFW/HcowuAt6UmrVAtBDi6KuIn8CEEJHAqWiLOyGlbJFS1nbazOvn1MU4fc1ZwAEpZeeJpV4/n050FauvCABChBABQCiOK026fU5VQji6p4G/ANajbDNdCLFVCPGlEGJ034TlQALfCCFybWtPd5YGHOnwuNDW5g3dxQreP6dZQAXwuu124StCiLBO2/jCOXUlTvD++exoLvCek3ZfOJ+ddRUrePmcSimLgCeAw0AJ2kqT33TazO1zqhJCF4QQs4FyKWXuUTbbhHY5ORb4D/BxX8TmxElSygnAecCtQohTOz3vbMURbw0v6y5WXzinAcAEYLGUcjxgAO7ttI0vnFNX4vSF8wmAECIQuBD4n7OnnbR5bQhkN7F6/ZwKIWLQrgAGAalAmBDi6s6bOdn1qOdUJYSunQRcKIQoAN4HzhRCvN1xAyllnZSywfb9F4BOCBHf14FKKYtt/5YDHwFTOm1SCAzo8Dgdx8vLPtFdrD5yTguBQinlOtvj5WhvvJ238fY57TZOHzmfrc4DNkkpy5w85wvns6MuY/WRc/ob4KCUskJKaQI+BGZ02sbtc6oSQheklPdJKdOllJlol47fSyntMrAQIlkIbb0/IcQUtPNZ1ZdxCiHChBARrd8DM4HtnTb7FLjGNupgGtrlZUlfxtkaX3ex+sI5lVKWAkeEEMNtTWcBOztt5vVz6kqcvnA+O7iSrm/BeP18dtJlrD5yTg8D04QQobZYzgJ2ddrG7XOqVkxzkxDiZgAp5YvAHOBPQggz0ATMlbbu/T6UBHxk+/sMAN6VUn7VKc4v0EYc7AcagWv7OEZ3YvWFcwpwG/CO7dZBPnCtj57T7uL0ifMphAgFzgZu6tDmi+fTlVi9fk6llOuEEMvRbl+Zgc3Ay8d6TtVMZUVRFAVQt4wURVEUG5UQFEVRFEAlBEVRFMVGJQRFURQFUAlBURRFsVEJQVEURQFUQlAUtwit9LFDKfQOz88TQjzXC687TwiR2uFxgRdnHCvHKZUQFKV/mIdWs0ZReo2aqawcd2xlMZah1W7xBx5Gm635byAcqESrHV8ihFgFbEGrqRQJXCelXG8rSfA0EII2G/VaKeUeN+NIAF4EMmxNd0gpfxFCLLK1Zdn+fVpK+axtn78BV6FVqawEctHWkJiENiO5CWhdBOc2IcQFgA64TEq52534FKUzdYWgHI/OBYqllGOllGOAr9CqUs6RUk4EXgMe7bB9mJRyBnCL7TmA3cCptiqiDwD/6EEczwBPSSknA5div67GCOActET0oBBCJ4SYZNtuPHAJWhJASrkc2AhcJaUcJ6Vssh2j0lY5djGwsAfxKYoddYWgHI/ygCeEEI8DK4AaYAzwra2Okj9aDflW7wFIKX8SQkQKIaKBCOANIcRQtJLBuh7E8RtglO01ASJbi/sBn0spm4FmIUQ5Wp2nk4FPWt/whRCfdXP8D23/5qIlEEU5JiohKMcdKeVeIcREtMJejwHfAjuklNO72sXJ44eBH6SUFwshMoFVPQjFD5je4RM9ALYE0dyhyYL2f9FZ/fqjaT1G6/6KckzULSPluGMbjdMopXwbbVWpqUCCsC1Ab7s903GVqyts7SejlQjWA1FAke35eT0M5Rtgfoe4xnWz/WrgAiFEsBAiHG351lb1aFctitJr1KcK5XiUDfxLCGEFTMCf0EoEPyuEiEL7u38a2GHbvkYI8Su2TmVb2/9Du2V0J/B9D+O4HXheCLHN9po/ATd3tbGUcoMQ4lNgK3AIrd9Ab3t6CfBip05lRfEoVf5aOaHZRhktlFJu9HYsAEKIcCllg60m/0/AjVLKTd6OSzkxqCsERfEtLwshRgHBwBsqGSh9SV0hKEoPCCGuBRZ0av5FSnmrN+JRFE9QCUFRFEUB1CgjRVEUxUYlBEVRFAVQCUFRFEWxUQlBURRFAVRCUBRFUWz+P2DfH5+snZGRAAAAAElFTkSuQmCC)
%% Cell type:markdown id: tags:
%% Cell type:markdown id: tags:
Finally, we define a vector with the names of the species, which will be used for plotting, and convert the species names to a categorical code. This makes classification simpler and also saves storage space. If we plot a random sample of the instances, it can be seen that each categorical code corresponds to a particular species.
%% Cell type:code id: tags:
``` python
iris['code'] = iris.species.astype('category').cat.codes
categories = iris.species.unique() # create a vector with the category names,
iris.sample(10)
```
%%%% Output: execute_result
sepal_length sepal_width petal_length petal_width species code
117 7.7 3.8 6.7 2.2 virginica 2
20 5.4 3.4 1.7 0.2 setosa 0
6 4.6 3.4 1.4 0.3 setosa 0
57 4.9 2.4 3.3 1.0 versicolor 1
109 7.2 3.6 6.1 2.5 virginica 2
86 6.7 3.1 4.7 1.5 versicolor 1
130 7.4 2.8 6.1 1.9 virginica 2
131 7.9 3.8 6.4 2.0 virginica 2
125 7.2 3.2 6.0 1.8 virginica 2
116 6.5 3.0 5.5 1.8 virginica 2
%% Cell type:markdown id: tags:
## kNN Algorithm
%% Cell type:markdown id: tags:
We first define a function which is helpful for plotting the decision boundaries of the kNN model. The first line defines the name of the function and its argument. The following lines enclosed in triple quotation marks correspond to a 'docstring' and will be printed when using the help facility for this function. Note that the function continues as far as the code is indented.
%% Cell type:code id: tags:
``` python
def plt_decision_boundaries(skm,xx,yy):
"""
Takes a sklearn model (skm) with two features xx and yy and plots the decision boundaries.
Takes a sklearn model (skm) with two features and plots the decision boundaries.
xx and yy correspond to matrices with the x and y coordinates.
"""
# ravel is a numpy method which converts a two-dimensional array of size (n,m) to a vector of length nm
# column_stack is a numpy function which takes two column arrays of length N
# and creates a two-dimensional array of size (N,2)
# now pass the (N,2) array to the model and predict values based on these features, zz will have size (N,1)
zz = skm.predict(np.column_stack([xx.ravel(), yy.ravel()]))
zz = zz.reshape(xx.shape) # reshape zz so it has the size of the original array xx, i.e., (n,m)
plt.contourf(xx, yy, zz, cmap=plt.cm.Paired) # plot the decision boundaries as filled contours
```
%% Cell type:markdown id: tags:
From the module `sklearn.neighbors` we import the `KNeighborsClassifier()` function. Then we can set a parameter `n_neighbours` which will control how many neighbors to use in the calculations.
%% Cell type:code id: tags:
``` python
from sklearn.neighbors import KNeighborsClassifier
n_neighbours = 5
```
%% Cell type:markdown id: tags:
We then set up our table of features `X` and our target values `y`. The features correspond to the first two features of the iris dataframe, and the target values are the categorical code for the species which was created earlier.
%% Cell type:code id: tags:
``` python
# we only take the first two features.
X = iris.iloc[:, :2]
y = iris['code']
```
%% Cell type:markdown id: tags:
To syntax to use the kNN algorithm is the same as other `sklearn` functions. We first instantiate the model by passing the appropriate parameters. In this case the two parameters are the number of neighbours to use and the weights for predicted the labels. The choice `weights='uniforms'` indicates that each of the neighbours contribute equally to predicting the target value. The alternate is `weights='distance'` in which case the contribution of neighbours decreases with distance. Once the model is setup, then we train the model by fitting the features `X` and labels `y`. Then the model can be used to predict labels.
The syntax to use the kNN algorithm is the same as other `sklearn` functions. We first instantiate the model by passing the appropriate parameters. In this case the two parameters are the number of neighbours to use and the weights for predicted the labels. The choice `weights='uniforms'` indicates that each of the neighbours contribute equally to predicting the target value. The alternate is `weights='distance'` in which case the contribution of neighbours decreases with distance. Once the model is setup, then we train the model by fitting the features `X` and labels `y`. Then the model can be used to predict labels.
%% Cell type:code id: tags:
``` python
clf = KNeighborsClassifier(n_neighbours, weights='uniform')
clf.fit(X, y)
```
%%%% Output: execute_result
KNeighborsClassifier()
%% Cell type:markdown id: tags:
To view the output of the model we can plot the decision boundaries in feature space. We setup an x and y table which cover the feature space using the `numpy` routines `meshgrid()` and `linspace()`. Then for each point in these tables the model predicts the label using kNN and then plots the boundaries where each label applies. We can then overlay the scatter plot we viewed earlier for comparision. As can be seen the boundary between setosa and the other two species is fairly clear, but that between versicolor and virginica is quite irregular.
%% Cell type:code id: tags:
``` python
x_min, x_max = X.iloc[:, 0].min() - 1, X.iloc[:, 0].max() + 1
y_min, y_max = X.iloc[:, 1].min() - 1, X.iloc[:, 1].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max),
np.linspace(y_min, y_max))
plt.figure(figsize=(8, 6))
plt_decision_boundaries(clf, xx, yy)
sns.scatterplot(x=X["sepal_length"], y=X["sepal_width"], hue=iris['species']);
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
If the number of neighbours is increased to 15 the boundary between the versicolor and virginica species becomes much more regular, as including more neighbours corresponds to averaging over a larger region.
%% Cell type:code id: tags:
``` python
n_neighbours = 15
clf_15 = KNeighborsClassifier(n_neighbours, weights='uniform')
clf_15.fit(X, y)
plt.figure(figsize=(8, 6))
plt_decision_boundaries(clf_15, xx, yy)
sns.scatterplot(x=X["sepal_length"], y=X["sepal_width"], hue=iris['species']);
```
%%%% Output: display_data
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)
%% Cell type:markdown id: tags:
## Confusion Matrix and Accuracy
%% Cell type:markdown id: tags:
We will now look at how we can assess the accuracy of the 5 neighbours kNN model, which we have called `clf`. This will involve introducing the concept of a confusion matrix, and the function below provides a simple method for viewing this.
The concept of accuracy and confusion matrix which we introduce here apply to classification models in general.
%% Cell type:code id: tags:
``` python
def plt_confusion_matrix(cnf_matrix, cats, method):
"""
Given actual target values and predicted values for a classifier 'method',
plots the confusion matrix
"""
# write the confusion matrix to a dataframe with row and column names as the categories
cmatrix = pd.DataFrame(cnf_matrix,columns=cats,index=cats)
f, ax = plt.subplots(figsize=(7,6)) # initialise the plots and axes
sns.heatmap(cmatrix, annot=True, linewidths=.5) # plot the confusion matrix as a heatmap
plt.title('Confusion matrix for '+method) # add a title, + concatenates two strings
plt.ylabel('Actual label') # add a ylabel
plt.xlabel('Predicted label') # add a xlabel
# adjust the bottom and top of the figure, so we can view all of it
bottom, top = ax.get_ylim() # get the y axis limits
ax.set_ylim(bottom + 0.5, top - 0.5); # adjust the y axis limits
```
%% Cell type:markdown id: tags:
We introduce the `train_test_split()` function to split our data into training and testing sets, and a function for calculating the confusion matrix and accuracy for classification models.
%% Cell type:code id: tags:
``` python
from sklearn.model_selection import train_test_split # import the splitting method from sklearn
from sklearn.metrics import confusion_matrix # import the confusion matrix function
from sklearn.metrics import accuracy_score # import the score functions
```
%% Cell type:markdown id: tags:
We first split the data into testing and training sets, as to test the accuracy of the model we want to use data that has not been used to train the model. Then we train the model using the training data, and then predict the output of the model for our testing features `X_test`. These predictions, `y_pred`, can then be compared against the actual values, `y_test`, to assess the accuracy of the model.
%% Cell type:code id: tags:
``` python
X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=0)
clf = KNeighborsClassifier(n_neighbours, weights='uniform')
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
```
%% Cell type:markdown id: tags:
The first way we can assess the model is to view the confusion matrix. This shows how and where the model makes correct and incorrect predictions. For each label that is predicted by the model this counts the number of times it has correctly predicted the species and the number of times where it should have predicted one of the other species. For example, the model has predicted versicolor seven times. Five of these times it has correctly predicted the species, and twice it has incorrectly predicted the species when it should be virginica.
%% Cell type:code id: tags:
``` python
cnf_matrix = confusion_matrix(y_test, y_pred) # create a confusion matrix for our actual and predicted values
plt_confusion_matrix(cnf_matrix, categories, 'kNN')
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
We can now calculate the accuracy of the model. This is simply the total number of correct predictions, i.e., the sum of the terms on the main diagonal of the confusion matrix, divided by the number of predictions, i.e., the sum of all the terms in the confusion matrix. In this case we have 11+5+4=20 correct predictions, and 11+5+8+2+4=30 total predictions. Therefore the accuracy in this case is 2/3. For a very good model the accuracy will approach 1, whereas for a very poor model the accuracy will approach 0.
%% Cell type:code id: tags:
``` python
print("Accuracy:",np.round(accuracy_score(y_test, y_pred),3)) # calculate and print the accuracy score
```
%%%% Output: stream
Accuracy: 0.667
%% Cell type:markdown id: tags:
## Normalization
%% Cell type:markdown id: tags:
For many algorithms, and especially algorithms which are based on the calculation of distances, it is important to normalize the feature variables. For example, consider if one of the features varied from 0.4 to 0.6, whereas another feature varied from 200 to 300. Hence the closest neighbours will be ones for which the second feature is approximately equal to the value of the instance that we are trying to predict, and therefore the second feature is largely redundant for our calculations.
The way to address this is to normalize the variables by the standard deviation of the feature. Recall that the standard deviation is the distance on either side of the mean value in which approximately 70% of the observations lie. In the example above the standard deviation will be approximately 0.1 for the first feature and approximatley 50 for the second feature. If we normalize (divide) by the standard deviation then the first feature will now vary from 4 to 6 and the second feature will also vary from 4 to 6. Hence the distances in each feature direction are now equally weighted, and each feature has an equal contribution to the kNN algorithm.
For the Iris Dataset normalization does not have a huge effect as the the features have similar standard deviations, however we will explain the process. For the exercise example, the normalization does have an important effect.
For the Iris Dataset normalization does not have a huge effect as the the features have similar standard deviations, however we will use this to explain the process. For the exercise example, the normalization does have an important effect.
We first show the standard deviations of the two features we are using. As is apparent they have similar magnitudes.
%% Cell type:code id: tags:
``` python
X.std()
```
%%%% Output: execute_result
sepal_length 0.828066
sepal_width 0.435866
dtype: float64
%% Cell type:markdown id: tags:
We now create a new normalized feature table `Xn`, which is each column of `X` divided by the standard deviation of that column. Then we repeat the calculations from earlier to plot the decision boundaries of the model. Again the decision boundaries between the virginica and versicolor species are fairly irregular.
%% Cell type:code id: tags:
``` python
Xn = X/X.std()
n_neighbours = 5
clf = KNeighborsClassifier(n_neighbours, weights='uniform')
clf.fit(Xn, y)
x_min, x_max = Xn.iloc[:, 0].min() - 1, Xn.iloc[:, 0].max() + 1
y_min, y_max = Xn.iloc[:, 1].min() - 1, Xn.iloc[:, 1].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max),
np.linspace(y_min, y_max))
plt.figure(figsize=(8, 6))
plt_decision_boundaries(clf, xx, yy)
sns.scatterplot(x=Xn["sepal_length"], y=Xn["sepal_width"], hue=iris['species']);
```
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
We then calculate the calculate the confusion matrix and accuracy. In this case the accuracy drops slightly. However, it is best practise to alwasy normalize variables when using the kNN algorithm.
We then calculate the calculate the confusion matrix and accuracy. In this case the accuracy drops slightly. However, it is best practise to always normalize variables when using the kNN algorithm.
%% Cell type:code id: tags:
``` python
X_train,X_test,y_train,y_test=train_test_split(Xn, y,train_size=0.8,random_state=0)
clf = neighbors.KNeighborsClassifier(n_neighbours, weights='uniform')
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
cnf_matrix = confusion_matrix(y_test, y_pred) # create a confusion matrix for our actual and predicted values
plt_confusion_matrix(cnf_matrix, categories, 'kNN')
print("Accuracy:",np.round(accuracy_score(y_test, y_pred),3)) # calculate and print the accuracy score
```
%%%% Output: stream
Accuracy: 0.633
%%%% Output: display_data
![](data:image/png;base64,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)
%% Cell type:markdown id: tags:
## Exercises
%% Cell type:code id: tags:
``` python
penguins = sns.load_dataset('penguins') # load the dataset from seaborn
penguins.dropna(subset=['bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'],
how='any',inplace=True)
penguins['code'] = penguins.species.astype('category').cat.codes
penguins.head() # display the first few lines
```
%%%% Output: execute_result
species island bill_length_mm bill_depth_mm flipper_length_mm \
0 Adelie Torgersen 39.1 18.7 181.0
1 Adelie Torgersen 39.5 17.4 186.0
2 Adelie Torgersen 40.3 18.0 195.0
4 Adelie Torgersen 36.7 19.3 193.0
5 Adelie Torgersen 39.3 20.6 190.0
body_mass_g sex code
0 3750.0 Male 0
1 3800.0 Female 0
2 3250.0 Female 0
4 3450.0 Female 0
5 3650.0 Male 0
%% Cell type:code id: tags:
``` python
penguins.describe()
```
%%%% Output: execute_result
bill_length_mm bill_depth_mm flipper_length_mm body_mass_g \
count 342.000000 342.000000 342.000000 342.000000
mean 43.921930 17.151170 200.915205 4201.754386
std 5.459584 1.974793 14.061714 801.954536
min 32.100000 13.100000 172.000000 2700.000000
25% 39.225000 15.600000 190.000000 3550.000000
50% 44.450000 17.300000 197.000000 4050.000000
75% 48.500000 18.700000 213.000000 4750.000000
max 59.600000 21.500000 231.000000 6300.000000
code
count 342.000000
mean 0.918129
std 0.892635
min 0.000000
25% 0.000000
50% 1.000000
75% 2.000000
max 2.000000
%% Cell type:code id: tags:
``` python
n_neighbours = 10
# we only take the first two features. We could avoid this ugly
# slicing by using a two-dim dataset
X = penguins.loc[:, ['flipper_length_mm', 'bill_depth_mm']]
X = X/X.std();
y = penguins['code']
categories = penguins.species.unique() # create a vector with the category names,
clf = neighbors.KNeighborsClassifier(n_neighbours, weights='uniform')
clf.fit(X, y)
x_min, x_max = X.iloc[:, 0].min() - 1, X.iloc[:, 0].max() + 1
y_min, y_max = X.iloc[:, 1].min() - 1, X.iloc[:, 1].max() + 1
xx, yy = np.meshgrid(np.linspace(x_min, x_max),
np.linspace(y_min, y_max))
plt.figure(figsize=(8, 6))
plt_decision_boundaries(clf, xx, yy)
sns.scatterplot(x=X["flipper_length_mm"], y=X["bill_depth_mm"], hue=penguins['species']);
```
%%%% Output: display_data
![](data:image/png;base64,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