I have other recourses but the platform won't let me upload them.CSC475 / SENG480B Music Retrieval...

Question

I have other recourses but the platform won't let me upload them.

CSC475 / SENG480B Music Retrieval Techniques Assignment 4 Fall 2020 J. Shier October 28, 2020 1 Introduction In this assignment we will explore machine learning topics including super- vised learning for genre classification with audio features as well as with song lyrics. There are three levels of engagement: Minimum, Expected, Advanced. Minimum is the least amount of work you can do to keep up with the course. If you are not able to complete the minimum work suggested then this course is probably not for you. Expected is what a typical student of the course should work on - the goal is to provide a solid foundation and understanding but not go deeper into more interesting ques- tions and research topics. Advanced is what students who are particularly interested in the topic of MIR, students who want a high grade, students interested in graduate school, and graduate students should work on. Each level of engagement includes the previous one so a student who is very inter- ested in the topic should complete all three levels of engagement (Minimum, Expected, Advanced). The assignment is worth 10% of the final grade. There is some variance in the amount of time each question probably will require. Please provide your answers as a single Jupyter Python notebook (.ipynb) uploaded through the BrightSpace website for the course. Also please answer the questions in order and explicitly mention if you have decided to skip a question. For the questions that do not require programming use the ability to write Mark- down in Jupyter notebooks for your answer. You are also welcome to do the assignment in any other programming language/framework. If you do so please include a PDF file with plots and figures for questions that re- quire them, as well as answers for questions that don’t require coding. Also include the code itself with instructions on how to run it in a README file. 1 2 Problems The coding problems mention specific libraries/frameworks for Python that can help you with implementation. However you should be able to find corresponding libraries or code for most programming languages if you want to try using a different programming language or environment. 1. This question will build on the audio feature extraction using spectral centroid question from assignment 3. We’ll perform experiments on three different genres: classical, disco, and reggae. There are 300 audio files in total, 100 for each genre. These audio files are available in the GTZAN folder in assignment resources. The assignment template contains a solution to the last question from assignment 3 (updated with new genres), which computes the mean and standard deviation of the spectral centroid for each track and plots them on a scatter plot. We’ll use these results for audio classification. If you’re not using Jupyter notebooks you will have to copy/translate this code into the language or environment that you are using. • a) Use sckit-learn to report the 10-fold cross-validation classifi- cation accuracy for a linear support vector machine and a naive bayes classifier trained on the two features calculated in the Jupyter template (mean centroid and std centroid) to predict the three genres. Show the confusion matrix for each case. (Minimum: 1 point) • b) Compute the MFCCs for each recording using the default set- tings of librosa. Then summarize the entire recording by taking the mean of the MFCCs across the recording as well as the mean and standard deviation across each recording. The resulting con- figurations will be just the mean (20 features per recording) and the mean and std (40 features per recording). Report on the 10-fold cross-validation classification accuracy and confusion ma- trix for these two configurations using the linear support vector machine and naive bayes classifier. (Minimum: 1 point) • c) Use t-SNE1 to reduce the dimensionality of the 300 by 40 feature matrix of mean and std mfccs to a 300 by 2 feature ma- trix. Visualize the corresponding scatter plot with coloring of 1https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE. html 2 the points based on genre. How does the visual separation/over- lap compare to the scatter plot of just the centroid? Run the 10-fold cross-validation classification accuaracy and show a con- fusion matrix for the same configurations as the full feature set, but now using only the 2 dimensions returned from t-SNE. How much accuracy is lost compared results from the previous ques- tion? (Expected: 1 point) • d) Let’s forget about the genre labels and do some unsupervised learning. In this question we’ll perform clustering on the two- dimensional results from t-SNE using K-Means2. Pretend that you forgot that the files in the dataset are actually from three different genres and experiment with a few different values for number of clusters. Plot the results of clustering using three different choices for number of clusters. Make sure to plot each sample colored by the label assigned to it by K-Means. What value for number of clusters gives you the best result in your opinion? How does your plot compare to the results from the previous question? Now, randomly select three points from two different clusters and play the associated audio files (6 in total). This is where keep- ing track of the audio file names during audio feature extraction would have been helpful (see code comment in the compute folder function above). Comment on the similarities between the audio files from the same clusters - do they sound like they are from the same genre? Does it make sense that they were clustered together? (Expected: 1 point) 2. This question will look at Naive Bayes classification with song lyrics. George covers material pretty much identical to this question in the first video of the Machine Learning for MIR Kadenze course, which you can use if you need to review this material before completing the questions. Our goal will be to build a simple Naive Bayes classifier for the MSD dataset, which uses lyrics to classify music into genres. More com- plicated approaches using term frequency and inverse document fre- quency weighting and many more words are possible but the basic 2https://scikit-learn.org/stable/modules/generated/sklearn.cluster. KMeans.html#sklearn.cluster.KMeans 3 concepts are the same. The goal is to understand the whole process, so do not use existing machine learning packages but rather build the classifier from scratch. We are going to use the musicXmatch3 dataset which is a large collec- tion of song lyrics in bag-of-words format for some of the tracks con- tained in the Million Song dataset (MSD). The corresponding genre annotations, for some of the song in the musicXmatch dataset, is pro- vided by the MSD Allmusic Genre Dataset4. For the purpose of this course, in order to simplify the problem, we are going to use a re- duced version of the musicXmatch dataset. Three genres are con- sidered, namely: Rap, Pop Rock, and Country. The resulting genre annotated dataset is obtained by an intersection of musicXmatch and MAGD, where we select 1000 instances of each genre, such that the three classes are balanced and easy to handle. In addition, we also reduce the cardinality of the dictionary of words used for the bag-of words lyrics representation (originally equal to 5000), to the 10 best words for each genre. Intuitively, the best words are the most fre- quent words for a particular genre that are not frequent among all the genres.5 The resulting dictionary of words is For answering this question we provide you with: • data.npy - the three genres dataset (not binarized - you will need to binarize) • labels.npy - the genre labels where Rap=12, Pop Rock=1, and Country=3 3https://labrosa.ee.columbia.edu/millionsong/musixmatch 4http://www.ifs.tuwien.ac.at/mir/msd/partitions/msd-MAGD-genreAssignment. cls 5The best genre words maximize the Term Frequency (TF) and Inverse Document Frequency (IDF) product. More details available at https://en.wikipedia.org/wiki/ Tf-idf 4 • dictionary.pickle - the full 5000 words dictionary • words.npy the 30 best word indexes with respect to the full dictionary • tracks.pickle - the track IDs of songs used (not needed for as- signment, but included for those interested). This is available on BrightSpace in the data folder in the assignment resources download. You will need to use python pickle and numpy load to load the pickle and npy files respectively. • a) Write code that calculates the probabilities for each dictionary word given the genre. For the purposes of this assignment we are considering only the tracks belonging to the three genres: Rap, Rock/Pop, Country. Use add-one additive smoothing6 to handle the case that there is no instance of a particular word in a genre. (Minimum 1pt) • b) One can consider the Naive Bayes classifier a generative model that can generate binary feature vectors using the associated probabilities from the training data. The idea is similar to how we do direct sampling in Bayesian Networks and depends on gen- erating random number from a discrete distribution (the unifying underlying theme of this assignment question). Describe how you would generate random genre lyrics consisting solely of the words from the dictionary using your model. Code that and show 5 ex- amples of randomly generated tracks for each of the three genres: Rap, Rock pop, and Country; each example should consist of a subset of the words in the dictionary. (Minimum: 1 point) • c) Explain how these probability estimates can be combined to form a Naive Bayes classifier. Code it and calculate the clas- sification accuracy and confusion matrix that you would obtain using the whole data set for both training and testing. Do not use any libraries such as scikit-learn but write the code directly. (Expected: 2 point) • d) Read the Wikipedia page about cross-validation in statistics7. Calculate the classification accuracy and confusion matrix using the kfold cross-validation, where k = 10. Note that you will need 6https://en.wikipedia.org/wiki/Additive_smoothing 7https://en.wikipedia.org/wiki/Cross-validation_(statistics) 5 to generate your own splits. Do not use any libraries such as scikit-learn but write the code directly. (Advanced: 2 points) 6

assignment-4-5mxcnh5q.pdf assignment-4-template-jjgmprhc-vytr14ey.ipynb downloads-q4204wz1-scawczlq.zip

Vicky · Accepted Answer

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CSC 475 / SENG 480B - Assignment 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os
",
    "import pickle
",
    "import numpy as np
",
    "import librosa
",
    "import sklearn
",
    "import matplotlib.pyplot as plt
",
    "from scipy import signal
",
    "import IPython.display as ipd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1
",
    "
",
    "This question will build on the audio feature extraction using spectral centroid question from assignment 3. We'll perform experiments on three different genres: classical, disco, and reggae. There are 300 audio files in total, 100 for each genre. These audio files are available in the GTZAN folder in assignment resources. Here is a solution to the last question from assignment 3 (updated with new genres), which computes the mean and standard deviation of the spectral centroid for each track and plots on a scatter plot. We'll use these results for audio classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_folder(folder):
",
    "    """
",
    "    Compute the spectral centroid calculations for a folder of audio files
",
    "    
",
    "    Notice that I'm also keeping track of the associated file names - don't really
",
    "    need it for this function - but will come in handy for the last part of question 1 :) 
",
    "    """
",
    "    
",
    "    results = []
",
    "    files = []
",
    "    for filename in os.listdir(folder):
",
    "        
",
    "        # Load audio file
",
    "        path = os.path.join(folder, filename)
",
    "        audio, sr = librosa.load(path)
",
    "        files.append(files)
",
    "        
",
    "        # Compute frame-by-frame spectral centroid
",
    "        sc = librosa.feature.spectral_centroid(audio)
",
    "        
",
    "        # Compute mean and standard deviation across frames
",
    "        results.append((sc.mean(), sc.std()))
",
    "    
",
    "    return np.array(results), files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "classical, _ = compute_folder('./a4_resources/GTZAN/classical')
",
    "disco, _ = compute_folder('./a4_resources/GTZAN/disco')
",
    "reggae, _ = compute_folder('./a4_resources/GTZAN/reggae')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=150)
",
    "plt.scatter(classical[:,0], classical[:,1], label="classical")
",
    "plt.scatter(disco[:,0], disco[:,1], label="disco")
",
    "plt.scatter(reggae[:,0], reggae[:,1], label="reggae")
",
    "plt.xlabel('Spectral Centroid Mean (Hz)')
",
    "plt.ylabel('Spectral Centroid Stdev (Hz)')
",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1a**
",
    "
",
    "Use sckit-learn to report the 10-fold cross-validation classification accuracy for a linear support vector machine and a naive bayes classifier trained on the two features calculated above (mean centroid and std centroid) to predict the three genres. Show the confusion matrix for each case. 
",
    "
",
    "(Minimum: 1 point)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Linear Support Vector Machine:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC
",
    "from sklearn.model_selection import cross_val_score
",
    "from sklearn.metrics import confusion_matrix, plot_confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.concatenate((classical,disco,reggae),axis=0)
",
    "y = np.concatenate((np.array([0]*100),np.array([1]*100),np.array([2]*100)),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC()
",
    "y_pred = clf.fit(X, y).predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.63333333, 0.4       , 0.53333333, 0.5       , 0.46666667,
",
       "       0.4       , 0.46666667, 0.33333333, 0.36666667, 0.36666667])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(clf, X, y, cv=10)
",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4466666666666666"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = scores.mean()
",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0, 94,  6],
",
       "       [ 0, 92,  8],
",
       "       [ 0, 34, 66]], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAsgAAAIhCAYAAABT4Ew8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAABcSAAAXEgFnn9JSAABc9UlEQVR4nO3dd5hdVdX48e9KL4QUSKiBAKF36SACKlVAwYaCil1fCwiCXdBX+Skqgopio/jaBVRApal0kCK9k9BJgISEJKRn1u+Pc4acTO4kM8mZTHLn+3me+5zMOfucs+/k5mbdddfeOzITSZIkSYVe3d0BSZIkaWVigCxJkiRVGCBLkiRJFQbIkiRJUoUBsiRJklRhgCxJkiRVGCBLkiRJFQbIkiRJUoUBsiRJklRhgCxJkiRVGCBLkiRJFQbIkiRJUkWf7u6AVn0RMREYBDzd3X2RJKmbjQZmZuba3d2RiLgE2KSLLj8uMw/vomt3OwNk1WFQ0GvIIFbbqrs7ouaz4Wazu7sLalJPjh/c3V1QE5o5/2WSlu7uRqtN+vWLrcaO6VvrRR97Yh5z52at11zZGCCrDk8PYrWt9ogDursfakJXXHtXd3dBTeqQHX3PUv1umPQ7Xpk/ZaX5RnXsmL7ce+2GtV5z232e5IFH5tZ6zZWNAbIkSVLTSlpqz2g3d/YYHKQnSZIkLcIMsiRJUpNKYEHWm0Fu/vyxGWRJkiRpEWaQJUmSmlhLj8j51ssMsiRJklRhBlmSJKlJJdQ+i0VPyEcbIEuSJDWtZEHWHdI2f4hsiYUkSZJUYQZZkiSpiTlIr/PMIEuSJEkVZpAlSZKaVAILas4g94R8tBlkSZIkqcIMsiRJUhOzBrnzzCBLkiRJFWaQJUmSmlRC7fMg94R8tAGyJElSE6t3Hb2ewRILSZIkqcIMsiRJUpNymrdlYwZZkiRJqjCDLEmS1MQW9ISUb83MIEuSJEkVZpAlSZKamLNYdJ4BsiRJUpMqBulF7ddsdpZYSJIkSRVmkCVJkppVQkvdKd8ekEI2gyxJkiRVmEGWJElqUtYgLxszyJIkSVKFGWRJkqQmVncGuScwQJYkSWpSCbSkJRadZYmFJEmSVGEGWZIkqYlZYtF5ZpAlSZKkCjPIkiRJTSoJFtScD80ekJE2gyxJkiRVmEGWJElqYnXPYtETmEGWJEmSKswgS5IkNSmXml42BsiSJElNbEFaMNBZ/sYkSZKkCjPIkiRJTStoqT0f2vyD/swgS5IkSRVmkCVJkpqUg/SWjRlkSZIkqcIMsiRJUhNzFovOM0CWJElqUgm0WGLRaX6kkCRJkirMIEuSJDWtYIHTvHWaGWRJkiSpwgyyJElSk0rqH6RnDbIkSZLUw5hBliRJamL1LzXd/AyQJUmSmlRmsCBrnuat5uutjPxIIUmSJFWYQZYkSWpi9U/z1vz8jUmSJEkVZpAlSZKaWEvN07z1BP7GJEmS1C0iYpeI+GNEPBcR8yJiakRcHxHvj4jFRgNGRO+I+ExE3BsRsyLixfL8LevslxlkSZKkJpVdsNR01rTUdES8FfgD0Bv4L3A9MBLYG3gt8Ebg6Er7XsCfgCOAqcDfgDWBtwFvioj9MvPWOvpmBlmSJEkrVET0AX5MERwfnZk7ZeY7M/P1wHbAS8C7I2K/ymkfoAiOHwW2yMy3Zea+wNuBQcBvyusuNwNkSZKkJragnAu5rkdNtgBGAQ9n5m+rBzLzQeDX5Y+7VA6dUG5PzsznK+0vAi4BxgJvrqNzBsiSJElNKilW0qvzkfV0bU4H200GiIiNgC2BWRSlFW1dWG4PW/6uGSBLkiRpxRsPjAM2j4h3Vw+UA+6OAaYAfy53b19u78vMeQ2u999yu10dnXOQnrQC9BvQwlGfeoF9Dp/CqPXmMX1qb26/ZggXnL4Okyf27e7uaSX31KP9+e2Za3H3jUOYPrU3I0bNZ9c3vsx7TpzI0DUWLPX8M04YzRW/XwOA7/35UbbZ7ZWu7rKaxOrD5/L2Y59gt9e9yMi1ZzN3Ti+ef24gd906gnPP3Ky7u6cOWrASTvOWmQsi4n3AZRS1wydS1BaPohik9wBwbGa+VJ6yQbl9pp1Ltu7fsI7+rXy/ManJ9O3fwrf/OI6jP/M8Awe3cPOVq/Pic/048Kgp/PjKR1h7g45+y6Se6K4bVuOTB23Gv/88gsGrL2DXN06jb78WLj1/JP9zwOa8+NySP2DddeNqXPH7NYio6UtR9Rhjt5zGTy+6ibe+90nmzw9uuXYkD907lCFD53HE0U91d/fU/TaJiPsbPTp6gcy8EdiHIpv8GuCdwH5AC3BVub/VauV2ZjuXa/3kP6QzT6I9TZ1BjohTgVOA92fm+d3bm8VFxBjgceDachTmirx3Ak9m5pgVed+e6N3HPc9WO8/kgdsH8YWjNmb2zN4AHPmRF/noqc9xwhlPc/LbxnZzL7Uymj0z+NYnNmTOrN4c/ZmJvPekiQBkwi/+d10uPGcU3z9xNKf9bnzD8+fODn7wufXZcPNZDB6ygAduX61hO6mt1YfP5X/P/i/9Byzga8dvz3+uHbXI8c22frmbeqbOSoKWmqZlq16zDhHxLuA84BbgXcD9wLrAZ4ETgf0iYs/MXOGZJDPIUhfq07eFw98/CYAffXG9V4NjgIt/NpLx9w9g+z1fYey27X0gVk924z+GMeXFvqy/yWyOOXHiq/sj4P1feI61Rs/hjmtXZ9z9Axqe/5sz1+K5x/vz6W89Qx8redQJx3xsHEOHz+OX399sseAY4JH7h3ZDr7SSGZeZWzd6dOTkiNgUuACYBByambdm5iuZ+WhmfpSi9OI1FFO7Acwot4PaueTgcjt92Z7OogyQu9ezFCMy39vdHVHX2HqXmaw2tIXnHu/HuPsW/zd9/d+GAbD7AdNWcM+0Knj0noEAbLv7DHq1ebfu0xe23qX4RvHmKxYPVh5/cAAX/mQUBx71kjXH6pR+/Rfw+kMmMGtmb666ZN3u7o5qsCB71fqoyVFAX+DyzJzR4Pgfy+3rym1rXc/67Vyvdf+TdXSuqUssVnblKMyHursf6jobbzULgMfuHdjweOv+jbacvcL6pFXH7JnFf0SrDW08EG/14cX+8Q8s+vpqaYGzTh7N4NUX8MEvP9e1nVTT2XSraQxabQH3/XcYc+f0Zue9JrHj7pPp26+FZ58cxPVXrcVLLzb+1kIrn4QuWEmvFq0BbXv1Oq37h5fbu8vtNhHRt8FMFq8pt/fU0blVNoMcEaMj4gcR8Ui5FvdLEXF7RJwSEasv5dyxEXFqRNwcERMjYm5EPBMRv4qIhsNyI2LDiPhJeb+Z5f3uj4ifRsTmbdpuExG/jojxETG7XCf8rog4MyLWqbQbExEZEde0c8/dIuL3EfFsRMyJiAkR8c+I+HCbdjtExOkRcUd5rznlvX8cEX7870Yj15sLwIsTGn+/Pancv9b6c1dYn7TqGLrGfABeeKZfw+MTn+rX8Pil563Jg3cM5sNffe7VIFrqqA02Lr5xeHlKP75yxl18/Ud3csQxT3HoO57hoyc9wi/+eiP7HDShm3upJtBaN7ZzO8dbFwh5AiAzHwceBAYCb2rQ/m3l9tI6OrdKBsgRsTfFJ4RPUaTnLwVuBIYCpwIbL+USHwK+SlGvchvF6ivTgPcAt0XEInPoRcRoivn1Plbu+jtwLcUk1x8G9qi03am85tEUdTB/pSg+7wscBywSTC/hOR4H3EQxonMCcDFwH7AN8J02zT8PfKb88w1l/wL4OHC7QXL3GTi4BYA5sxr/U2vNEA4cbBCjxW27exGo3PrP1Xl5cu9Fjk2a0Jf/Xl8M1p41Y+Hr68Xn+nL+t9dhuz2ns//bp6y4zqpprLZ6kZjb7XUvstOekzn7tC046vX7cOwhr+XCCzZkwMAWTvj6/Wy8WS2lnupqCS0ZtT5qSiH/tdy+LiI+Xj0QEbuzMK65sHLojHJ7ekSMqrQ/EjgceKxy3eWyypVYRMQI4CJgGHAScEZmtlSO7wEs7TvFvwA/LT+NVK/9fuBc4Ezg9ZVDHwJGAD/KzE+1OWcDiuC31aeBAcBnM/N7bdpuQftfJVTbvQ74PkVB+hGZ+c/KsT7AAW1O+SlwXHXZxYjoBXwZ+BrwDRYWuUtaRey0z3TGbjuTx+4dxJeP2YRPnPYMG242m8cfHMAPPjeaBfOLkeRR+fz1oy+uz7y5waf+X3tThUpLFuUEBX36JueeOZa//Wk0UGSRzj1zM9ZaZzZ7H/A8b33fE3znS9t2X0e1SsvM/0bEdylmrPhxRHyCYu7jdSkSj72An2Xm1ZXTzgUOAY4AHoqIfwJrUkwVNws4JjPn19G/VS5ApghWR1IUdX+37cHMvHlpF8jMW9rZf15EfBDYNyKGZmZrMDuy3F7d4Jy2k0EuqW1H640/T5EB/mY1OC6vMZ8iQ1zd9+8G92oBvh4RH6H4VLXcljC34SZ1XL8ZzXqliFz6D2xpeHzAoJayXe+Gx9WzRcBXf/k4X3nPxjxy9yCOe9PCCrDhI+dxzIkTueDb67xao3z934Zyy5VDeffxE9lgU+fX1rKZPWvh+9GVDQbpXXXJuux9wPNsu5PfUKwaovYaZGqa5i0zT4qImyi+od+J4lv26RTf0v88M3/Xpn1LRLyd4hv5DwCHUsx/fBFwSmY+UEvHWDUD5DeW258uz0UiYjWK9bp3oMgOt2aB16H4m9+EhcsW3lFuT4uIBcDVmdneqKo7gIOBsyPiy8ANnfk0U2aI9y1//FknzluDIhDehiK73voO1xdYIyJGVFaj0Qry4rNFbejIdRqtiglrlvufb6fGVFpr/Xn85KqHufEfQ3ng9sHMmd2LMZvNZr8jp3Dj34vZKzbcvHg7+s9Vxc93Xj+E+/6z6JzH4+4vBvL9+MvrMXj1FvZ/x0sc8E7fErS4FyYUA/Bmz+rFtCmLvzc9/1xxfOgIx05o+WXmn1m4nHRH2i+gKLU4Y2ltl8eqGCCPLrfjlvUCEfF64PcszPY2Ul2J5XyKsoZ3UNQ7z46I24DLgXMzc2Kl7XeA11IEuf8GZkTEzcDfgPMrWen2rEFRgP5SZnbo43k50fbPWLjKTHvPZ7n+N2xvbsMys7zV8ly7WbXOLjB221kNj7fuf/xBR4Srfb37wOsOe5nXHbbo28cDtxfTfm6/56IzJD14x2DaM+7+YrrB7fZoNKuSBOMeKv7769e/hT59W5g/b9Hs45DVi5xPdV53rbwSaKl5qemesC7nKjlIb3mUmeM/UtSsfJ0isBsM9MrMAFrT+a9+f5CZCzLznRRTiHyNYhDebsA3gUciYs9K22kU9ct7A6dT1NO8nqKu+eFyYuw6n8+GFAF8P+B4YFNgUGZG+XxaS07qXUZHHXL/bYOY8XIv1t1oLhtvvXiQvPebpgJwy5VLnHhFWsxLL/Thhr8NY/Xh89nr4KkAfPbMp7jiubsaPloD4u/9+VGueO4u3vPZiUu4unqyFycOZNzDq9GrFw3LKLYp9417yPetVcUCotZHT7AqBshPl9tlrXvdmyJLe1FmnpKZD2bmzMxs/UDU7gwYmXlnZp6ama+jyD5/nyIze2abdpmZN2Tm5zJzN4qC898Ba1EE1UsyiaLQfEREDOvA8zmEIjj+QWaelZmPZWY1ElvajB7qQvPn9eKS89YE4JOnPUP/gQtnqzjyIy+y8dazufumwTx2b3sLA6mne+KhAcydveh/SC8+15dT378RM2f05iOnPEv/gT0hn6MV6aLzxwDwoc88wvA1F9azb7zZdI58T7EOw98vbG+9BmnVtyqWWFwN7A98hGI2is5qnXB6sSHeETGWhRNNL1FmTouIL1BkbbdZStsXIuJUinXGl9Z2QTkv8sEUz/H0pXRlSc/ndRRBubrRb89aix33nsHWu8zkvBsf4r5bBzNqvXlsudNMpk7qwxknjF76RdRjXXjOKG78x1DGbjuTEaPmM3VSH+6/bTDz5vTi3cdPZP93OFBK9bvm8nXYcY/J7H/4BM656CYevHsY/fsvYMvtX6Zf/xb+cdF63HC1/72sCpLoghKL5s8ir4oZ5F9QZFkPjojjI2KRv6WI2L06N14Dj5TbIyPi1RrkMlv7Sxadsq312HsiolFgezBF6cLTlbYfi4iNGrQ9pNw+3eBYW9+mKPH5UkTs16YvfSLikMqu1udzTEQMrrRbDzinA/dSF5s3pxcnv30TfvP9tZgzqxd7HDiNUevP5co/DOcTB27KxKf6d3cXtRLb88CX2XKnV3jmsQHc8LehPPHQAHbedzqnX/gY7zvZMgl1ne+fsjVnfX1LJj4zkO12fonNtpnGYw8N4Xtf3ZoffsNhJ2puq1wGOTNfKqf4uISixOHT5YC5gcCWwFhgR+CFds6/PSKuoshCP1JZxW5fisD7r8Cb25z2VuBXETEOuJeiBGIjijrkFor5hlt9DPhJRDxAseLLfGALYHtgNkXd89Ke47URcTJF9vhfEXE78ChF3fT2QH+KmSoofw/3U6xE81hE3EgxD/N+wF0Ui43sibrV3Nm9+NV31uZX31m7u7uiVcyeB7/Mngcvdfr0pfrORY/V0Bv1LMEVf16fK/5sKcWqrqfUDddpVcwgk5nXUASK51BkcN8C7EWxCMdXWfoMF2+mqAV+kSILvBPFrBa7A1MbtD8DOJtibr69KSaoHgX8AdgtM/9UafsViomsE3gDxVRyAyky3ztk5o0dfI7fpZj4+s/ABhRLKG5DEaCfWGk3t+zTTygC8EMpPij8kOJDQOP5xSRJktTQKpdBblWugvfxpbQ5lWLp6bb7Z1Fkfb/c9hhwbPmotr8OuK6D/bqUDq4DnplPsITZJTLzeuD6DlxnCvA/7Rzet51z/DgpSVIPUHcNck/gb0ySJEmqWGUzyJIkSVqyBBa4UEinGSBLkiQ1raCl9kF6zV+laYmFJEmSVGEGWZIkqUlZYrFszCBLkiRJFWaQJUmSmlVCS90zu/aAFLIZZEmSJKnCDLIkSVKTSmBBzfnQHpBANkCWJElqXlF/iYXTvEmSJEk9ixlkSZKkJtZiPrTT/I1JkiRJFWaQJUmSmlSxUEi9NcM9YZCeGWRJkiSpwgyyJElSE6t/FovmZ4AsSZLUtIKWrLtgoPkDbkssJEmSpAozyJIkSU2qWEnPQXqdZQZZkiRJqjCDLEmS1MQcpNd5ZpAlSZKkCjPIkiRJTSqT2mexyB5QhGwGWZIkSaowgyxJktTEWnrAvMV1M0CWJElqWsGC2gfpNX/AbYmFJEmSVGEGWZIkqUklXTBIr9arrZzMIEuSJEkVZpAlSZKamAuFdJ4ZZEmSJKnCDLIkSVITc5q3zjNAliRJalLFIL16A2QH6UmSJEk9jBlkSZKkphW1T/PmQiGSJElSD2MGWZIkqVllF0zz1gOKkM0gS5IkSRVmkCVJkppUUv80bz0ggWyALEmS1MxcSa/zLLGQJEmSKswgS5IkNTEzyJ1nBlmSJEmqMIMsSZLUpJLogqWmmz8jbQZZkiRJqjCDLEmS1MSsQe48M8iSJElShRlkSZKkJlb3QiE9gQGyJElSk0rqL7HoCSvpWWIhSZIkVZhBliRJalbZBYP0ekAK2QyyJEmSVGEGWZIkqYk5zVvnmUGWJEmSKswgS5IkNSmXml42BsiSJElNLC2x6DRLLCRJkqQKM8iSJElNzJX0Os8MsiRJklRhBlmSJKlJudT0sjGDLEmSJFWYQZYkSWpizmLReQbIkiRJzSq7YCW9HlBjYYmFJEmSVGEGWZIkqWlFF5RYNH/JhhlkSZIkqcIMsqSV2oyW2d3dBTWp6Nevu7ugJhSxcmVXneZt2ZhBliRJkirMIEuSJDWx7Akp35qZQZYkSZIqzCBLkiQ1sZYeMOtE3QyQJUmSmpgr6XWeJRaSJEnqNhExMiK+GxEPR8SsiHgpIv4bEd9pp/1hEXFtREwrH9dExJvq7JMBsiRJUpNqneatzkedY/4iYifgQeBEYB7wV+AWYATwmQbtjwcuAfYEbgT+BewKXBYRn6yrX5ZYSJIkaYWLiJHA5cBA4M2ZeUmb47u2+Xlz4LvAHGC/zLy53L8ZcBPw/Yi4PDMfW96+mUGWJElqVllM81bno8YU8teANYGT2gbHAJl5a5tdxwG9gXNag+Oy3SPANykSv8fV0TEDZEmSJK1QETEQOAZ4BTivg6e11hlf2OBY677DlrNrgCUWkiRJTW0lncViZ2AIcENmzoqIg4H9gQHAI8AfM/O51sYRMQzYoPzxzrYXy8ynI2ISsGFErJ6Z05ancwbIkiRJTSu6IEAOgE0i4v5GRzNz6w5cZKty+0JE/AV4c5vjp0XEBzPzd+XPrcHxlMx8pZ1rPkNRsrEhcG8H+tAuSywkSZK0og0vt4cDBwGfAEYBYygG4g0ELoiIHcp2q5XbmUu4ZmvgPGR5O2cGWZIkqYm1dE2JxbgOZorb05qk7QN8KTN/XDl2UkRsCLwdOAk4ejnus1ydkyRJklaUGZU/Nxqk17pvnzbtBy3hmoPL7fTl6BdgBlmSJKlpJeXUbDVfswZPltuZmflig+NPlNtR5fapcjs8Iga3U4e8fptrLzMzyJIkSVrRWmeiGBgR/RscH1FuZwBk5lQWBsk7tm0cEaMpBug9ubwzWIABsiRJUlPLjFof9fQpnwLuppgSY58GTVr3Vad0+1u5fVuD9q37Lq2jfwbIkiRJzSq7IECur2Tj9HL73YhYp3VnOXPFieWP51TanwUsAD4WEbtX2m8KfAmYX7ZZbtYgS5IkaYXLzN9GxAHA+4AHIuImiund9gT6Az/PzD9V2j8cEScBZwDXR8RVwFzggPK8T2fmY3X0zQBZkiSpidU8Rq9u7wduBD4K7EvR3f8CP83MC9o2zszvR8RjFNO/7V3uvh04PTMvq6tTBsiSJEnqFpmZwM/LR0fPuZSaao3bY4AsSZLUxOpfarr5OUhPkiRJqjCDLEmS1MxW8iLklZEZZEmSJKnCDLIkSVITswa585Y5QI6Ic5fjvpmZH1yO8yVJkrQUCWTNJRY9oWJjeTLIxy7HuQkYIEuSJGmlszwB8n619UKSJEldILqgxKL5SzaWOUDOzGvr7IgkSZK0MnCQniRJUrNKoO4Mcg8oQq49QI6IPsCbgF2BNYH/ZOa55bF1y30PZOb8uu8tSZIkLa9aA+SIeC3wa2A0RYFKAn2B1hkv9gD+CLwduLjOe0uSJGlxdc9i0RPUtlBIRGwFXA6sA/wQeAeLV3FfCswE3lrXfSVJkrQEWfOjB6gzg/wVYABwSGZeCRCxaHycmXMj4r/AjjXeV5IkSapNnQHyfsCtrcHxEjwLbF/jfSVJktQOV9LrvNpKLIBhwNMdaDeYoi5ZkiRJWunUmUF+ARjbgXZb0rFAWpIkScurh9QN16nODPK/gB0iot0V9iLiCIog+qoa7ytJkiTVps4A+VvAXOAvEfHxiFi79UBEDI+IDwC/BF4BzqjxvpIkSWpHZtT66AlqC5Az8yHgXeU1f0QxGC+B9wGTgJ8D/YGjM/Pxuu4rSZKkdtQ9xVsPmeqtzgwymfkXYBuKeZAfAmZTZJXHAz8FtsvMS+q8pyRJklSn2peazswngePrvq4kSZKWRc8oi6hTrRlkSZIkaVVXewY5IvpTLCW9N7Buufs54AbgosycXfc9JUmS1I4eUDNct1oD5Ih4I3A+sA6L5/M/ApweEcdmptO8SZIkaaVUW4AcEbsBlwH9gP8AvwOeKA9vSDHDxe7ApRGxT2b+p657S5IkqR1mkDutzgzy/1IsIf3xzPxpg+M/jIiPAOcAXwcOrPHekiRJUi3qHKS3G3B7O8ExAJn5M+A2ikyyJEmSulpGvY8eoM4AuQV4rAPtHsNkvyRJ0gqRWe+jJ6gzQL4V2K4D7bYr20qSJEkrnToD5K8Am0bE1yJisetG4WvApmVbSZIkdTWXme60ZR6kFxHvbbD7AuDLwHsi4iLgyXL/hsCRwBjg58DmFDNdSJIkSSuV5ZnF4nwaf44IikD4xMrxakX3R4APA79ajntLkiRpabpiYF0PGKi3PAHy1+kxiXZJkiT1FMscIGfmqTX2Q5IkSV0gTGd2Wq1LTUuSJGklY4DcaXXOYiFJkiSt8mrPIEfEa4E3U0znNoRFB+i1ysx8Q933liRJUhs9YFBd3WoLkCMigF8C72NhUJwsGiC3/myyX5IkSSulOkssPgYcC9wB7A9cXO7fHDiYYlq4FuA7wMY13leSJEntcaGQTquzxOJY4BXg4MycHBHHAGTmo8CjwBUR8XfgD8BNLFxERJIkSVpp1JlB3hK4KTMnlz8nQET0bm2QmRdSZJg/W+N9JUmS1B4zyJ1WZ4DcC5hc+XlmuR3ept2jwLY13leSJEmN1B0c95Aguc4A+Vlg3crPrSUUO7Zptxkwv8b7SpIkSbWpswb5v8AbIqJ3Zi4ArgS+DZweEe+iCKA/BuwE/LPG+0orvX4DWjjqUy+wz+FTGLXePKZP7c3t1wzhgtPXYfLEvt3dPa3knnl0ABeetT733rQ6M6b2Yfioeez0him888RnWH3EovmGcfcM5varhnP3dUN5+tGBzJ3Vi6FrzmPr3afx5o9PYMxWM9u5i7SoTbecyluPGc9W27/E0OFzmT2rN0+MG8JVl47m6svWp/EsrlopOc1bp9UZIF8CvBN4E3BJZt4dEb8HjgLur7SbD3ypxvtKK7W+/Vv49h/HsdXOM5k8sQ83X7k6a60/jwOPmsJub5zOcYeOZeJT/bu7m1pJ3Xvj6vy/YzdnzqzerDd2FpvvNJ2nHx7E5ReszW1XDuf//fV+1lh3LgAL5sPJhxQVbKsNm8fmO01nwKAWHr9vMNf9eSQ3/W0Njv/BY+xx6Evd+ZS0Cthzvwl8/ht30rtP8thDq3P/XSMYOnwuW+/wEtvsMIUddpnEd09p+wWx1DxqC5Az83cRcTGLlk+8D7gHeAtFLfIjwOmZeWtd911ZRcSpwCnA+zPz/HLfNcA+wEaZ+UR39U0r1ruPe56tdp7JA7cP4gtHbczsmcW41SM/8iIfPfU5TjjjaU5+29hu7qVWRnNm9eLMT45lzqzevP34Zzjqs88AkAm/+sYGXPLTdTn7pI356m8eevWcsdvP4K2ffpad3jiF3uUQ6ZYW+P131+eiH6zP2Z/dmK33nLZY5llq1at3C/9z8n307pOc/pUduPbK9V49NnrMdE7/2c3sd9BzXHnJaO65Y81u7Kk6KnpAzXDdal1qOjPnlOUVrT/Py8xvZebumbl5Zh6WmdfXeU9pZdanbwuHv38SAD/64nqvBscAF/9sJOPvH8D2e77C2G392luLu+XvI5j6Yj/W3WQW7zjhmVf3R8DRn3+aUaNnc/e1w3jigUEA9O4D3/7bfex64MLgGKBXL3jXSc+w7iazmDWjD3f8c9gKfiZalYze8BWGj5jL008MXiQ4Bnj6iSH8+/Ji36Zbvtwd3ZNWiFoDZC3Veymmw3u2uzuiFWPrXWay2tAWnnu8H+PuG7TY8ev/NgyA3Q+YtoJ7plXB+HsHA7DVbtPo1ebduk/fZIudpwNw6xVtJwtaXARsuGXxQWzK8/3q7aiayrx5HQsNpr/s+IlVhjNYdJoB8gqUmU9l5kOZOa+7+6IVY+OtZgHw2L0DGx5v3b/RlrNXWJ+06pg9s3iLXm3ogobHVxtelEm0ZpCX5oWy1n3YSN+C1L6Jzw7iuacHMXrMK+xzwKL5nNFjprPfQc8y/eW+3HTt2t3UQ6nrLXMNckSMX477ZmZushznrzQi4nDgC8D2wCzgmvLnRm2voUENckRsCHweeAOwPjAbmADcAJyRmQ+3uc5o4CTgIGB0ed/xwKXA9zNzWqXtIOBEigGUmwBzgbuBH2fm75fnuWvpRq5XDJ56cULjTMukcv9a689dYX3SqmP1NYoA+MVnG2d8X3i6CHgnPbv0QZ4P3jqEcfesRp9+Ley479Ta+qjm09ISnPH17Tn1e7dx8v/exZFHj+fZpwczrByk99Tjq/H9r2/PjGl+E6HmtTyD9MbU1YlVVUR8DPgJxRcO11MEtbsDt1IEqx25xmiKKfJGUCyi8negN7Ah8GHgZuDhSvu9KWYMGQY8Ud5nILAFcCrwV+Cusu0Q4N8UU+u9CFwGDAZeD+wdEXtk5nHL8tzVMQMHtwDFYKtGWjOEAwc3zhCqZ9tqt2lc/MP1uOOfw5n2Up9FBtZNntCXe64bBsCsGb3buUJh5vTenH3ixgAc+qEJDF/LDLKW7MF7RvC5j+/Bl799B2O3mMbYLYq8y7y5vbjr1jWZ+GzHvrXQysFBep23zAFyZvbo8owy6/t9YB5wWGZeUe7vC5wHHNPBS32IIjj+UWZ+qs09NgD6Vn4eAVxEERyfRJFdbqkc3wN4rnKJ0yiC438Db87M6WW7LYBrgU9HxFWZeVkHn/P97Rxqim8DpJXNDvu8zMbbzmD8vavxjfdswYe/8TjrbzaLpx4axDmf24gF5eeqWMK78YIFcOanxjLh8YFsusOMV2fCkJZknwOe5fgv38ND9w3j9K/syJPjV2ONkXM48ujxHHn042y302RO/NCezJ+35A9n0qqqRwe5y+kDwADgd63BMRQzdwDHsXCp7aUZWW6vbnugrFkeV9n1obL95Zn53WpwXLa/OTNfAIiIwcAHgRbgf1qD47LdQ8A3yh/NIHehWa8U/8T6D2xpeHzAoJaynf/JaHERcNLPH2H05jMZd/dqfP6wbTlm81354pu3Ydrkvq/ObLHa0PanbPvZFzbijquHs+4ms/jirx6ibz9TSVqydUe/wme+ejfTXu7H107chUceGMac2X147unB/Ohb2/Kf60cxdotpHHCYH7ZWGRn1PnqAOhcK6Wn2LreL1fFm5uSIuJJi/ueluaPcnhYRC4CrM7O9EVtvLLc/7cB1d6Iovbi9DIjb+j/gB8BeEdGrbbDdSGZu3Wh/mVneqgN96nFaa0dHrtP4K+01y/3PP2Mtnxobtf5cvnfFPfzn8hE8fPsQ5s7uxejNZrL3EZP4zz9GADB6s8afx//vtNFc/du1WHPdOZzyuwed+1gd8rr9n6Nv3+SOm0cye9biYcL1/1yH3fZ+gW12fIm/X7xhN/RQ6noGyMtu3XL7ZDvHn+jgdc4HDgDeQVFPPDsibgMuB87NzImVtqPL7TiWrrV/DfuRmVMj4mVgKMUiLpM72F91wvgHilkqxm47q+Hx1v2PPzhghfVJq57efWDPQ19izzYr4D18xxAAtt5j8WkC//LjdfjLj9dj6Jpz+ervHmTNdR0Iqo5Zc1SRo3llRuMQYeaMovJvtSHWsq8SumJqth7wRZQlFt0sMxdk5juB1wBfA24DdgO+CTwSEXt25e278NoC7r9tEDNe7sW6G81l460XD5L3ftNUAG65cvUV3DOt6qa80Jeb/zaCIcPnsdvBiwbOV/1mFP932oYMHjqfr/zmIdbbxGkE1XFTJhezorS3EMimW00F4PkJjaevlJqBAfKym1Bu2/t+qVPfO2XmnZl5ama+jqLO+PvAEODMSrOny21HBsW1DtZr2I+IGEox2G8WMKUzfVXHzZ/Xi0vOK5Zi/eRpz9B/4MLZKo78yItsvPVs7r5pMI/d64hwNfbUQwOZO3vRmr/Jz/Xj2x/YnFkz+vC+rz5J/4ELP+vefNkIfvaFjRgweAFf+tVDbLS1qzSqc265bi0Atn3NSxxy5KJfkm6+zRTectTjANz4r3VWeN+0jFwopNMssVh21wP7UpRG/KN6oJxt4oBlvXBmTouILwDHA9tUDl0N7A98BPjLUi5zB0Xwu1NEbJqZj7Y53jrLxo0dqT/WsvvtWWux494z2HqXmZx340Pcd+tgRq03jy13msnUSX0444TRS7+Ieqy//nRdbr18OBtt8wrDR83j5cl9eei2Icyb04u3HfcM+7190qttX57UhzM/PZaWlmDU6Nlc+eu1uPLXay12zV0PfIndDvJzsRob9/BQLvr1xrz1mPF84nP38aa3PcHTjw9hxMjZbLFNsYz5P/68AXfdtmZ3d1Ud5DRvnWeAvOzOA04Gjo6I32Tm1fDqNG/fp5hveKki4j3AnZl5X5tDBwPBwqwxwC8opnc7OCKOB87KzFdf9hGxOzA+M1/IzFci4lzgE8DZEXFEZr5SttsM+HJ52g8686TVefPm9OLkt2/CUZ96gf3eMoU9DpzG9Km9ufIPw7ng9LWZNMEBemrfrge+xNQX+vLEg4N4+PYhDB46nx32ncqhH5zINnsuWns8Z1Zv5s8tvhh86qHBPPVQ47ehUevPMUDWEp37wy158J7hHHzkk4zd4mXW3/AVZr3Sh/vuXIMr/jqaa69cr7u7KHUpA+RllJmPR8SJwI+AKyLiOmAixUIhw4HfAEd34FJvBX4VEeOAeymyvhtR1CG3sDCQJTNfioi3UywU8n2KeYxvo5itYktgLLAj8EJ5yhfK/uwPjI+Ia1m4UMgA4AeZ2aEFTbR85s7uxa++sza/+o5Ls6pzdjtoSoeD2VGj53DRM7d0cY/UU9x87drc7HLSzcEMcqd1WQ1yRGwaEXuU2cqmlJlnA0ewcGDdgRTLOO8OPNbBy5wBnA1Mp5g67ghgFPAHYLfM/FObe15Dsaz1ORQZ5rcAewEvA1+lMsNFOffxPsApwCTg8PIetwPvdhU9SZKkxdWaQY6I/hTB2EcosqgAF1AsqkFEHAOcAHwgM++q897dJTP/QuN64FPLR7Xtvg3Ovw64rpP3fBz4eAfbvgJ8vXxIkqSexgxyp9WWQY6IgcA1wOeAucDfKTKcVf+iyH6+o677SpIkSXWqs8TiZIoyg3OBjTPzsLYNMvM54AEWrggnSZKkLhRZ76MnqLPE4p3AU8DHM3NJ65k+TFEzK0mSpK6UQLb9Qr+Gaza5OjPIGwG3LyU4hqL8YvhS2kiSJEndos4M8iw6FvhuhCu3SZIkrRg9IONbtzozyHcBO0fEyPYaRMRGFPP03lbjfSVJkqTa1Bkg/xwYAvwuIhZbfzIihlEM4OsL/KzG+0qSJKmBoP5BejVXNK+UaiuxyMzfRcRhwFEUq7bdVB7aKyL+SrFgxerArzLzsrruK0mSJNWp7pX0jqaYB3k2cEC5b1PgMIoKmC8B76/5npIkSWpP1vzoAWpdSS8zE/hORJwBvAYYQxGEPwPclplz67yfJEmSVLdaA+RWmbmAYiCeg/EkSZK6UU9Z3KNOXRIgS5IkaSVhgNxptQXIEXFuJ5pnZn6wrntLkiRJdakzg3xsB9okxewgCRggS5IkdaWuGFjXAzLSdQbI+7WzvxcwmmJWi6OA7wOX1nhfSZIkqTZ1zoN87VKa/Coi/gZcAFxS130lSZLUPgfpdV7d8yAvUWb+DrgfOHVF3leSJEkrt4hYIyJeiIiMiMeW0vbYiLg1ImZExEsR8feI2LOuvqzQALn0KLBzN9xXkiRJK6/vAWsurVFEnAmcB2wDXA3cCuwPXBcRb6mjIys0QI6IXsB2QMuKvK8kSVKPtQqspBcRbwDeB/x8Ke3eCBwHTAa2z8y3ZOZBwOuABcB5ETFsefuzQgLkiBgUETsAv6NYenpp9cqSJEnqASJiIPBT4AHgu0tpfkK5/UZmPtq6MzNvBs4BhlHDTGl1zoO8oCPNgBeBk+q6ryRJktq3CgzSOwXYGNgHmNdeozKQfn3544UNmlwIfBo4jKJcY5nVOc3b07SfeJ8LTKDIHJ+dmS/UeF9JkiStgiJiO+BE4LzMvD4ixiyh+eZAf+DFzHymwfH/ltvtlrdfdU7zNqaua0mSJKkmXZNB3iQi7m94u8ytO3KBcmzaL4CpwMkdOGWDctsoOCYzX4mIqcDwiBiSmdM70o9GaqtBjojDI+Lguq4nSZKkpvYpYBfgpMyc3IH2q5XbmUto80q5HbI8HauzxOLPwJXAP2q8piRJkpZH12SQx3U0U9xIRGwAfAO4NjPPr61XNakzQH4RmFLj9SRJkrQ8sgsG6dVzvbOBfsDHOnHOjHI7aAltBpfbZS6vgHoD5GuAXSMiMnPlHy8pSZKk7nIoRe3xORFR3T+g3K4XEdeUfz4qMycCT5U/r9/oghExmGKatynLU38M9QbIXwFuA74fEZ/PzNk1XluSJEnLYuVNWw6jmNqtkQGVY61B88PAHGBkRKyXmc+2Oec15fae5e1YnQHyu4C/UxRcHxURV1NE+o0C5czM/63x3pIkSVpFZGY02l9O8/Y4RY3z2DbnzIqIfwEHA28Hzmxz+tvK7aXL279lDpAjYjzwp8z8XLnrVIrPKAGMAt69hNMTMECWJEnqYqvAQiGdcQZFgPzliPhb62p6EbEH8FGKso1fLu9NlieDPAYYWfn5/cvXFUmSJKl9mXl1RJwFHAfcFRFXUQz2258iSfv+zJy6vPepc6GQC+q6liRJkmrSXBlkMvP4iLgL+CRFYDwXuBr438y8qY571FmDLEmSJC2zzHyCIhO8tHbnA+d3VT8MkCVJkppZk2WQV4TlDZB3iIivLsuJmfn15by3JEmSliCof5DeUtO7TWB5A+Tty0dnBMVnGQNkSZIkrXSWN0AeB9xYR0ckSZJUs6T+EoseULKxvAHyDZn5gVp6IkmSJK0EHKQnSZLUzHpAxrduvbq7A5IkSdLKxAyyJElSE2uypaZXCANkSZKkZmaA3GnLHCBnpuUZkiRJajpmkCVJkpqYJRadZxZYkiRJqjCDLEmS1MzMIHeaGWRJkiSpwgyyJElSs3Kp6WVigCxJktTEors7sAqyxEKSJEmqMIMsSZLUzHpASUTdzCBLkiRJFWaQJUmSmpgLhXSeGWRJkiSpwgyyJElSMzOD3GlmkCVJkqQKM8iSJEnNzAxypxkgS5IkNTEH6XWeJRaSJElShRlkSZKkZpXUX2LRAzLSZpAlSZKkCjPIkiRJTSqovwY56r3cSskMsiRJklRhBlmSJKmZ9YCa4boZIEuSJDUxp3nrPANkSSu11377hO7ugprUWuc/1d1dUBPK9wNPdncvtLwMkCVJkpqZGeROc5CeJEmSVGEGWZIkqZmZQe40M8iSJElShRlkSZKkZpVdMItFD8hIGyBLkiQ1sx4Q0NbNEgtJkiSpwgyyJElSE4s0hdxZZpAlSZKkCjPIkiRJzcwEcqeZQZYkSZIqzCBLkiQ1sdqneesBzCBLkiRJFWaQJUmSmpkZ5E4zQJYkSWpWrqS3TCyxkCRJkirMIEuSJDWzHpDxrZsZZEmSJKnCDLIkSVKTCuqvQY56L7dSMoMsSZIkVZhBliRJambWIHeaAbIkSVITcyW9zrPEQpIkSaowgyxJktTM0hRyZ5lBliRJkirMIEuSJDUrl5peJmaQJUmSpAozyJIkSc2sB2R862aALEmS1MSipbt7sOqxxEKSJEmqMIMsSZLUzCyx6DQzyJIkSVKFGWRJkqQm5lLTnWcGWZIkSaowgyxJktSskvqXmu4BGWkzyJIkSVKFGWRJkqQmZg1y5xkgS5IkNTMD5E6zxEKSJEmqMIMsSZLUpIL6Syyi3sutlMwgS5IkSRVmkCVJkppZ3dO89QBmkCVJkqQKM8iSJElNzGneOs8AWZIkqVkl9U/z1gMCbkssJEmSpAozyJIkSU3MEovOM4MsSZKkFSoiBkXEWyLilxHxcETMjohXIuLuiPhqRKy2hHOPjYhbI2JGRLwUEX+PiD3r7J8BsiRJUtNKaKn5UU8R8ruBPwMfABYAlwDXAxsBXwNui4hRbU+KiDOB84BtgKuBW4H9gesi4i11dAwMkCVJkrTizQN+BmyVmVtl5jsy8yBgc+BOYAvgzOoJEfFG4DhgMrB9Zr6lPOd1FEH2eRExrI7OGSBLkiQ1s6z5UUeXMi/IzI9m5oNt9k8APlH+eGRE9KscPqHcfiMzH62cczNwDjAM+GAd/TNAliRJamKR9T5WgLvLbX9gDYCIGAi8vtx/YYNzWvcdVkcHDJAlSZK0Mtm43M4DXir/vDlFwPxiZj7T4Jz/ltvt6uiA07xJkiQ1qwSy5rRvcblNIuL+hoczt17OOxxXbi/PzDnlnzcot42CYzLzlYiYCgyPiCGZOX15OmAGWZIkSSuFiDiEoo54HvCVyqHWad9mLuH0V8rtkOXthxlkSZKkJtZFdcPjasgULyIitgB+DQRwUmbevZRTuowZZEmSJHWriFgPuBwYDpyRmWe1aTKj3A5awmUGl9vlKq8AM8iSJEnNbSVfajoiRgBXAhtSLALy2QbNniq367dzjcEU07xNWd76YzCDLEmSpG5SLin9D2Ar4GLgw5kNRxU+DMwBRpbZ5rZeU27vqaNfBsiSJElNLDJrfdTWr4j+wF+BXYErgHdl5oJGbTNzFvCv8se3N2jytnJ7aR19M0CWJElqZi01P2oQEb2B31Es/nE9cGRmzl3KaWeU2y9HxKaVa+0BfBSYCvyyjv5ZgyxJkqQV7ZPAEeWfJwE/johG7T6bmZMAMvPqiDiLYp7kuyLiKqAfsD/FzBfvz8ypdXTOAFmSJKlJFctD1ztKr6Zp44ZX/nxEu63gVIoAGoDMPD4i7qIIsPcH5gJXA/+bmTfV0jMMkCVJkrSCZeapFMHvspx7PnB+fb1ZnAGyJElSM1vJp3lbGRkgSytAvwEtHPWpF9jn8CmMWm8e06f25vZrhnDB6esweWLf7u6eVnLH7HI3O46ewKYjX2L4oFn07zOfSa8M4o6n1uWC/+zAYy+usdRrnHPUJey+0bMAHPCj9/DC9NWWcoZ6jKkL4PfT4ebZ8MJ86B+wVh94TX/46LDF22fCFTPh8lfgiXkwJ2GN3rBlPzh6dRjje5pWfQbIUhfr27+Fb/9xHFvtPJPJE/tw85Wrs9b68zjwqCns9sbpHHfoWCY+1b+7u6mV2Af3/C8D+87j0RfX4NEXRwCwyZovcdi2j3DQVo9xwsUHcv1jY9o9//BtH2L3jZ6lJaFXwzEw6rEemQufmwTTWmBMH9hzIMxMeHIeXDhj8QB5bsJXJ8Ftc2BIL9i6fxFQT5gP18yCXQcYIK+Maq5B7gkMkKUu9u7jnmernWfywO2D+MJRGzN7Zm8AjvzIi3z01Oc44YynOfltY7u5l1qZfebCg3hg4kjmLlj0Lfsdr7mPLx54PaccfA0H/ui9LMjFZ+4cPnAWJ7zhJm4aP5oxI6ay7rDlXmBKzWLqAvj8pCLo/d81iuC46qEGM259f0oRHL9pMHxiWBEct5q8AOYbiK2MahpU16M4D7LUhfr0beHw9xeDb3/0xfVeDY4BLv7ZSMbfP4Dt93yFsdvO7K4uahVw17PrLBYcA/zxv9vw1JTVWXO1WWy85pSG5560/40M6DOf067Yu6u7qVXNBdPg5Rb4yNDFg2OALfot+vNDc+HKmbBFX/jMsEWDYyjKLNYy76bmYIAsdaGtd5nJakNbeO7xfoy7b9Bix6//2zAAdj9g2grumZrF/AXF2/i8BYu/ne+58VMcsvWj/OKmnXhm6tAV3TWtzOYkXD0TBgQcNLhj5/ztlWL7ltWg8Xy1Wlll1vvoAfyoJ3WhjbeaBcBj9zbIzlT2b7Tl7BXWJzWPN23zMGPWmMqTLw3lqSmLBsAD+s7jSwdex/hJwzj/lh26p4NaeT08t6g13qZfkQn+zyy4Yw7MS1i/D+wzCNbsveg5d5bvU1v3h+fmw79mwgsLYFgv2GUAbOtYCjWPHhcgR8QY4HHgWuBwijn4jgDWB84uJ6AeAZwEvBnYiGIS6juAMzLzsnaueyRwMrAdMBP4N/AF4BjgFIrVXc5vc852wDeBvYHewJ3A/wLzyvMvyMxjK+3XAd4DvAkYC4wEXgJuAv5fZt7WTt8GUaw68w6gdWnG+4CfZOYFS/yFabmMXK+o4XtxQuNBK5PK/Wutv7TVNSV43253ssmaUxjYdx4brTmFsSOn8ML0wXz+r/vT0qb++H/2vo31hk3ng79+M/NberdzRfVYT84rtsN7w1cmwU1tPqT/chp8dji8vvzma27ChAXFn++cDT+cWvxP1eo302HfgfD5EdDX7PJKJSFqWh66es1m1+MC5IqBFEHyhuX2v8CUiNiMYkWW0cATwBXAEGB34NKIOCkzv1u9UEQcB5xJsUL5dcBEYDfgVuDSRjcv1w2/GhgE3AM8AGwCXA6c3U6f3wx8G3i4PGcaRcB7BHBoRByamVe2uc8o4CqKwH1i+VwD2BM4PyJ2zsxPLfE3pWU2cHDxrjRnVuNqptkze5XtFqywPmnVtcdGT786VRvAc1OH8OXLXs+DE0cu0m6LtV7k3bvcwyX3bM4dT6+7orupVcGMMmK6aVZRbPnpYbDPwKL04i8z4I8z4NsvwQZ9YGy/he0Bzppa1Cx/YHUY0RvumgNnTClmsRj1cuOp4aRVTE8OkHcFbgY2bl23OyJas7ijKbLB38vMlvLYWOBK4FsRcXlm3lfu3xg4nSLLfFBm/rvc3wf4GfD+tjeOiF4UK8AMAr6UmadVjn0Q+EU7fb4R2CYz729zvQOBSyjWMd80c5ECofMoguOzgM9l5pzynLWAy4BPRsTfMvPypf7GJHWrj/3+cACG9J/D2JGT+ehr7+DcY/7Kj67dlV/ctBMAvaKFUw65humz+3PGv/bozu5qZdYa7y4APjAU3lyZF/ujw+D5BXDtLPjjdPjiGgvbQxE0f3XEwjkDXzsQ+gJfnFwE18esDoMd4rTy6Iq64eZPIff0V/CnW4Pj0mHAtsBFmfmd1uAYIDMfA06kKIX4cOWcDwD9gP9rDY7L9vOBE4AZDe77emAz4FHgW9UDmflLikB4MZl5b9vguNx/BfAnigz0Nq37I2IH4BDgNuCE1uC4POd54CPljx9vdL+2IuL+Ro/yvmpg1ivFP7H+Axt/vzVgUEvZzq/A1XHT5/TnzmfW5ZN/PIT7J4zkf153K1uv8wIAR+9yD1uuPYkz/707U2c1rn2XGFgpgzho8QHEHFjuu3vu4u33H7z4hNq7DSxqkefSeHo4da+s+dED9OQM8oTMvL3NvgPK7cXtnHN9ud21sm+vcvunto0zc2pEXAkc2eZQ6zkXVYPwij9U2iwiIvoDB5V9GEkRnEMR2ENRcnFv+efW5/OXRvfJzDsjYkab56Mavfhs8dczcp15DY+vWe5//pl+DY9LSzK/pTdXPrgJW6/zIq8b+wT3TxjFPmOfpCXhsG0f5rBtHlmk/RqrFdMJfueIK5k3vzfn3rIjN43foDu6ru7WOh3bgIBhDT6gr10en1qWfw3uBUMCpies3c4H+rV7w9SW4iGt4npygPxUg31jyu1vIuI3Szh3zcqf1ym3T3fiPstyDhGxLUUpxZgl9G1I5c+t7b4ZEd9cwjkDlnDsVZm5dTv9uh/YqiPX6GnGP1Bk8MZuO6vh8db9jz/Yob8CaTFTZxavseGDFr7GegXsvMGEds/Zfr3nAbjk3s27tnNaeY0tBw7PyWIAXr82GeHpZZBbzRxv0q+oN57eTgA8PRc/RyuF6CFTs9WpJwfIjebVai05uRx4fgnnTqq/O0sWEQH8kSLoPad8jAdmZGZGxGkUs2ZU35lan88NwLgV11u1uv+2Qcx4uRfrbjSXjbeexfj7F/3Ke+83TQXglitX74beqRnstMFzADxTTvP2od++ud22f//4r1l32HQO+NF7eGH6au22Uw+wVh/YpC+Mmwf3zIGd23xIv7usyBtb+XZrzwFFgHz3HDi0zevn+fkwcX55jktNa9XXkwPkRp4pt7/IzIs6eM4EYHOKgX0PNDg+up1z2jvW3v4tysftmdmoZnjjBvtan89fMvN77dxLXWj+vF5cct6avPv4F/jkac/whaM2Zs6shUtNb7z1bO6+aTCP3dugBlACdlhvAoP6z+Pm8aPJyuffPr0W8LYdH+BN2zzCrHl9uOJBlytXJ71zCJz2EpzzMny7b7ESHsBjc+FP5fCZwyqLiBw0uJjO7ZpZsN+shavvzW4pZrZYAOw2AEYZWqx0zCB3mq/iRV0FfJBi2rSOBsg3AvsCb6WYEu5VETGUhXXAbc8BOCIivthm1gko5itua3i5fabtgYgYDuzf4JyrKOZVPgIwQO4mvz1rLXbcewZb7zKT8258iPtuHcyo9eax5U4zmTqpD2ec0N7nJAk2GPEyXz/037w0cwAPThzJy7MGMGzgbMaOnMyoITOZPa83p1y2H8+bEVZnvWEQ3D67WD76AxOLBUDmJNw/p5jj+E2DiwVDWg3uBV8YAV+eBF+dXCxFvUYveHAuTG4papA/M7zd20mrkp4+i0VbF1FkgY+OiK+UA+JeFYW9IqI6gO48inG7742I11Xa9qYISqs1wa3+RTGDxeYU08lV73EsxcIhbT1GMdHO6yNi00r7ARTlFiPanpCZ/6EIkveKiLMjYrHv8SNi+4g4qMH9VJN5c3px8ts34TffX4s5s3qxx4HTGLX+XK78w3A+ceCmTHzK1afUvtufWpdf3Pganpw8jE1HTmb/Lcaxw/oTmDa7P7+9fVve/st3cuVDZo+1jE4eDicMg3X7FKUTD82FTfuV+xsEu7sMgB+PKqZ2e24+3DK7qF9+62rF/pHOyLPSSYrooc5HD0hIm0GuyMz5EfEWikzw1ynmCL4HeIFiYN4OwCjgM5RZ4MwcFxEnUywU8u+IuJaifnlXiqD11xSr6c2t3KclIt5HsVDItyLiXSxcKGQXioVCPtHmnBci4pcUU8zdHRH/AmaxcBW+84FjGzytYyhqqv8HeHdE3AU8BwylmB95NMUcyc6D3IXmzu7Fr76zNr/6ztrd3RWtYp57eXV+dN1utVzrkJ8cU8t11EQi4E2rFY+O2qQfnLpG1/VJWgmYQW4jMx8FdgS+TFHOsDvFNG2bUSwi8gmKoLd6zlnA24Dby/YHAndRrKbXOhhwcptzbqZYze4yiuWsD6f4UusQigVMFjuHYr7iEymWyn4DRXB8NbAz8GQ7z+eF8j6fpgjCdyz7uh3FIL+TgO82OleSJK36IrPWR0/Q4zLImfkEi8700KjNy8A3y0dHr3sRbeqWyzKLPSm+jLi7wTl3UyxOQpvzzin/eFeb9guAM8pHW6eWj0Z9mw38sHxIkqSepIcEtXUyg1yDiNgkIoa12defYgnqrYB/ZubENsdHRMSYBtd6J/AhYCpFdlmSJEkrUI/LIHeRtwNfi4g7KBb/WB3YnmJBkEnAJxucsxlwc1njPL7ctyXFwL0FwEcz85Wu7rgkSWpyZpA7zQxyPf5JsTz1OsCbgP0oBtD9BHhNZj7c4JzxFIPx+pbtD6UYOHcxsHdm/nEF9FuSJEltmEGuQWbeBryrk+e8QOPMsiRJUn3aWR1c7TODLEmSJFWYQZYkSWpWXTE1Ww+oaTZAliRJamY9IKCtmyUWkiRJUoUZZEmSpGZmBrnTzCBLkiRJFWaQJUmSmpkZ5E4zgyxJkiRVmEGWJElqZi4U0mkGyJIkSU0qktrnQY4eULFhiYUkSZJUYQZZkiSpmTlIr9PMIEuSJEkVZpAlSZKaVkJL3Rnk5s9Im0GWJEmSKswgS5IkNTNrkDvNDLIkSZJUYQZZkiSpmZlB7jQDZEmSpGaV1B8g94B42xILSZIkqcIMsiRJUjOrfZq35mcGWZIkSaowgyxJktS0ErKl/ms2OTPIkiRJUoUZZEmSpGbmNG+dZoAsSZLUrJL6B+n1gHjbEgtJkiSpwgyyJElSM7PEotPMIEuSJEkVZpAlSZKamRnkTjODLEmSJFWYQZYkSWpmZpA7zQBZkiSpaSW0uJJeZ1liIUmSJFWYQZYkSWpWSf0lFs2fQDaDLEmSJFWZQZYkSWpmDtLrNDPIkiRJUoUZZEmSpGbWYga5s8wgS5IkSRVmkCVJkppUkmTWOw9y9oBpLAyQJUmSmlVSf4lF88fHllhIkiRJVWaQJUmSmpnTvHWaGWRJkiSpwgyyJElSM2upd5BeT2AGWZIkSaowgyxJktTMrEHuNANkSZKkZpVJ1l1i0QMCbkssJEmSpAozyJIkSc2sB2R862YGWZIkSaowgyxJktTM6l5qugcwgyxJkiRVmEGWJElqZulCIZ1lBlmSJEndIiIGRsTXI+KRiJgdEc9FxLkRsV539ssMsiRJUrPKJOuuQa5pVoyIGAD8C9gdmAD8FRgDvB84NCJ2z8zxtdysk8wgS5IkNbNsqfdRny9TBMc3A5tl5jszczfgRGAkcG6dN+sMA2RJkiStUBHRD/hk+eMnMnNG67HMPAO4B9gnInbqjv4ZIEuSJDWxbMlaHzXZCxgKjMvMOxscv7DcHlbXDTvDAFmSJEkr2vbl9r/tHG/dv90K6MtiHKSnOoyeyQxuziu7ux9qQvN/fX13d0FNavxf53V3F9SEZj73MsDo7u5Hq5nM4OaWy2u/JrBJRNzf6Hhmbt2By2xQbp9p53jr/g0717t6GCCrDjOTFl5h2tPd3ZFVwCbldly39mJV8tK07u7BqsLXVifNeam7e7DK8LXVOaOBmd3didK48v/nrrj28n4IWK3ctve7eqXcDlnO+ywTA2Qtt8xcu7v7sKpo/bTdwU/XUof52lJX8bW16srMw7u7D6sqa5AlSZK0orXOWjGoneODy+30FdCXxRggS5IkaUV7qtyu387x1v1ProC+LMYAWZIkSSva3eX2Ne0cb91/zwroy2IMkCVJkrSi3Qi8TDEbxg4Njr+t3F66wnpUYYAsSZKkFSoz5wI/Kn88OyJaa46JiBMo5j++NjPv6I7+RWZtK6JIkiRJHRIRA4BrgN2ACcD1FPMe7wa8COyemeO7pW8GyJIkSeoOETEQ+ALwboq5lV8CLge+kpntLSLS9f0yQJYkSZIWsgZZkiRJqjBAliRJkioMkCVJkqQKA2RJkiSpwgBZkiRJqjBAliRJkioMkNXjRMSpEZERcWx396WRiBhT9u+abrh3RsQTK/q+PV2j12REXFPuG9N9PZOknskAWZIkSaro090dkLSYZ4EtgZnd3RF1q/cCgyheD5KkFcgAWVrJZOY84KHu7oe6V2Y+1d19kKSeyhILNZWIGB0RP4iIRyJiVkS8FBG3R8QpEbH6Us4dW9aC3hwREyNibkQ8ExG/iojN2jlnw4j4SXm/meX97o+In0bE5m3abhMRv46I8RExOyJejIi7IuLMiFin0m6JNcgRsVtE/D4ino2IORExISL+GREfbtNuh4g4PSLuKO81p7z3jyNi3Q7/UlWbiDi8fH3NjIjJEXHREl5bDWuQO/OaK9t3+N9ERAyKiK9ExH1l25cj4rqIOKrWX4Q6rPp+EBGrR8QZEfF4RMyLiDPLNiMi4v9FxAOVv7d/RcShS7jukRFxS/kamhQRf6q8BzYcoxER20XEpRExNSKml6+N/SNi3/Kc89u0XyciTo6Ia8v3q7nle+vFEbHLEvo2KCK+EBF3RsSM8nFLRLxvmX+RUicZIKtpRMTewD3Ap4C+wKXAjcBQ4FRg46Vc4kPAV4HBwG3AJcA04D3AbRGxXZv7jQb+C3ys3PV34FpgDvBhYI9K253Kax4NTAf+CtxS9vM4YLHApp3neBxwE/BOYAJwMXAfsA3wnTbNPw98pvzzDWX/Avg4cLtB8ooVER+j+HvfjeK1cBWwE3ArsEkHr9Hh11zZvsP/JiJiCHAd8HVgFHBZ2XZX4HcRcVbnnrFqNpDi7/pY4C6K96cp5Qesuyj+vQ8ErgBup3idXRoRn217ofJ95CJgF+A/LPpa3KjRzSNiD+Bm4FDgSYrXxwDgcuDIdvr8ZuDbwFoUr8M/A88BRwA3RsQBDe4zqrzPacDa5XO+DtgCOD8iftjOvaR6ZaYPH6v8AxgBvAAk8FmgV5vjewCjyj+fWrY7tk2b3YGNGlz7/WX7f7XZ/7Vy/w8bnLMBsEnl5wvKtic2aLsFsE7l5zFl22vatHsd0EIRtL+hzbE+wCFt9u0HrNVmXy+KDwEJnNugLwk80d1/n832ADYEZgFzgQMr+/sCvy5/74u8JoFryn1jlvE11+F/E+XPP2x9nQND2rw+ny+PHdrdv8ue9qi8HyTFh+NhlWO9KQLPBE6q/h0DY4HxwHxgm8r+jSk+UM0B9qvs7wOc285rsRfwcLn/i23698HKOee3ObYtsHWD53Rgef/HgGhz7G/ltc4E+lf2r0XxwTKBg7r778VH8z+6vQM+fNTxAE4u3zj/0YG2p7b9D6AD59xQBqdDK/t+XF7nzR04/+9l2+070Lb1P8Rr2rnG52r4fT0DTGqw3wC5Cx6VwPaCBsfWAF5pEJRcw+IBcmdec535NzGYYlDoAmCLBsc/VV7rqu7+Xfa0R5sAeec2x95S7r+wnXOPKI+fVdn3jXLfLxq0H0bxDVfb1+Iby32P0OaDVnn8hkYB8lKeV+sHw20r+3Yo993azn12LI//tbv/Xnw0/8NBemoWbyy3P12ei0TEasBhFG/UIygyfADrUJQnbELxFTfAHeX2tIhYAFydmbPbufQdwMHA2RHxZeCGzJzfiX71AfYtf/xZJ85bAzicogRjGEXGCYrntUZEjMjMlzp6PS2zvcvt79seyMzJEXElRbCzNJ15zXXm38ROFF/P356ZjQaI/h/wA2CviOiVmS0duKbqNSEzb2+zr7VE4eJ2zrm+3O5a2bdXuf1T28aZObV8LbYtmWg956J2/u7/UGmziIjoDxxU9mEk0K88tG253RS4t/xz6/P5S6P7ZOadETGjzfORuoQBsprF6HI7blkvEBGvpwhgRi6h2ZDKn8+neEN/B0Vt5+yIuI2iJu/czJxYafsd4LUUQe6/gRkRcTPF14nnZ+bLS+neGhQBzEuZOaWDz+ddFMH0akt5PgbIXa+13vvJdo4/0cHrnE/HX3Od+TfR2r+G/SgDp5cpapeHA5M72F/Vp9GsJmPK7W8i4jdLOHfNyp9bBwQ/3Yn7LMs5RMS2FLXSY5bQt+p7amu7b0bEN5dwzoAlHJNqYYAs8Wrm+I8UWeOvUwTKTwKzMjMj4rfAuyiyyABk5gLgnRHxLYrBKK+nGBizN/D5iDgoM28q204rA/C9KDLU+5bt9we+EBF7Z+ajNT6fDSmCKYDjKQLxZzNzVnn8Jooa1Gh0vlZOnXnNdcXtu+i66phG3xS0DrS/nKJOvD2T6u/OkkVEULynjgHOKR/jgRnle+ppwBdY9D2o9fncwHIkO6Q6GCCrWTxNMZhoExZ+XdcZe1NkaS/MzFMaHG93BozMvBO4Ezi1nDbrVIrZI86k8lVgZibFG/8N8Opo7TMpAu9vUmQF2zOJYpDXiIgYlplTl/J8DqH4KvO7mdlo9oGlzeihek2gmKlkQ+CBBsc37MzFOvia68y/ieeW1I+IGEpRojML6NA3GFohnim3v8jMizp4TutrcTSNX4ujG+ybsIRj7e3fonzcnpkfb3C80XtQ6/P5S2Z+r517SSuE07ypWVxdbj+yjOcPL7fPtD0QEWOB13TkIpk5jSIrkhR1v0tq+wJFYEMH2i6gGLQFHXuOS3o+r6MYEa4Vp7UWdLEPQRExgoW1l522hNdcZ/5N3EER/O4UEZs2OH5Mub3R+uOVylXl9ohOnHNjuX1r2wPlB6FGr8XWc44oM8NtNfpwv6T3oOEU3561tSzPR+oSBshqFr+gyLIeHBHHt30Tj4jdy4xtex4pt0dGxKs1yBExDPglCwfrVa/5nohoFNgeTPG14dOVth+LiEbzix5Sbtur7av6NkUQ9KWI2K9NX/pExCGVXa3P55iIGFxptx7FV51asc6jmNbq6IhoHTxHRPQFvk8xi8RSdeY1Ryf+TWTmKxRTfPWiGEhafc1sBny5/PEHHemnVpiLKLLAR0exwEv/6sEo7BUR1QF051FMN/je8sNya9vewPdYtCa41b+ARykyzye3ucexLByEWvUYxcw/r69+6IqIARTvQSPanpCZrXMy7xURZ0eDxZ0iYvuIOKjB/aR6dfc0Gj581PWgqOudRhFEjqcYWX0JxRt7AjuU7U6l8TzIV5b7p1BMaP/n8s+PAn8pj+1bad+677Gy7W8pJrhvoZgu6+2VtneVbe8HLqSocW7dNwvYq9J2DA2meSuPfba8flLMCfrbst/PA1Mr7fpRLCCSFF+PXkgxsf8rFNmgG2kzhVh5ntO8dd3r8xPl73cBxUDN3wGPA1NZOOXVsZX217T9O+rMa64z/ybKtkMoFpjI8vX0R4ra9Vm0mSrMxwp93bT7flAe37T8u239e7sK+A3FgiGt81cf3+ac4yqvxX+Vr8VxFO93/1cee3ebc/Zg4XSEd5Wvvf+Ur70flft/1uacn5X7Z5bvP38CJgIvUgTqjd6HR1HMFNT6Xvzv8vlcRjEYMIEzu/vvxUfzP8wgq2lk5jXA9hTZiaCYNmsv4GWKxTGWNujjzRS1wC9SZOR2oghkd6cIYto6AzibYt7QvSm+FhxFEYTslpnVaZS+wsJJ+N9AMVBvIEWWb4fMvJEOyMzvAvtQBEcbAG+j+Fr9XuDESru5ZZ9+QjG451BgS4rFIPYH5nXkfqpPZp5N8Rq5jWJg3YHA3RSvr8c6eJnOvOY69W8iM6dTvLZOocg8H17e43aKYOm4zj1jrQhZDO7dkSLL/wzF6+lIYDOKOvVPUHwAq55zFsV7x+1l+wMpgt7dWDgYcHKbc24G9qQIVDeieH3Mo/gW7OZG51Cs2nkixQfBN1C8nq4GdqadGV2yKD3bE/g0RXZ8x7Kv21F8EDgJ+O4SfylSDSLTgcmSJPV0ZZnFPRQfptfNRacNXNJ55wAfBY7KzD90YRelFcYMsiRJPUhEbFKOr6ju6w+cDmwF/LNtcBwRIyJiTINrvRP4EMW3bJd1UZelFc5p3iRJ6lneDnwtIu6gGNi5OkUpzjoU5TWfbHDOZsDNEXEPRakDFJnmzSlqmT+axWBPqSlYYiFJUg8SEbsAJ1DUH4+kSJY9SzGw7/9l5mKz6pQznnyVYnGadSlmXpkE3EQx3/rNbc+RVmUGyJIkSVKFNciSJElShQGyJEmSVGGALEmSJFUYIEuSJEkVBsiSJElShQGyJEmSVGGALEmSJFUYIEtaqUVEtnm0RMTUiLg+Ij4UEdHN/Tu27NepbfafX+7ft1s6towi4pqy32M62L7h81/Gez8REV0+Of+q+ncjacUxQJa0qrigfPwGeADYC/g58Nvu7FRXqjP4lCR1XJ/u7oAkdURmHlv9OSL2B/4OHBURv8nMy7qlY+37AvAt4Knu7ogkqXPMIEtaJWXmVcD/lT++pRu70lBmTsjMhzJzZnf3RZLUOQbIklZld5bb0a07ypKEJyKiX0R8NSIeiog5EfGXSptBEfGFiLgzImaUj1si4n3t3Sgi9oqIqyNielkDfUVE7LaE9u3WuUbE4Ij4XETcHhHTIuKVsp9nR8RmZZtrgPPKU05pU4d9bJvrbVne7+nyuT4fEb+PiK3b6VvviPhsec/Z5XlnRcTq7T2fzoqIdSLi5Ii4NiKejYi5ETExIi6OiF2Wcm5ExHER8UDZv2cj4gcRMWwJ7d8VEf+KiCnlOQ9GxKkRMaiu5ySp57DEQtKqbEi5ndNmfy/gL8DrgGuBe4DJABExCrgK2A6YWB4PYE/g/IjYOTM/Vb1YRBwK/JniPfNWYDywPXAdcH5nOhwR65T33xqYAlxT9n9j4GPAo8AjwOXl/fYC7gbuqlzmscr13gL8HuhftrmF4gPDO4DDIuLgzLyuTTd+DRwFzASuBOYD7yvvNa8zz2cJ3gx8G3iY4vc/DdgUOAI4NCIOzcwr2zn3h8BHKH439wL7AJ8C9omIvTNzWmvDiOhVPp93ATOA2yl+rzsDpwAHR8S+mTmrpuclqQcwQJa0Sipnrzi0/PGeNodHUwSdm2fms22OnUcRHJ8FfC4z55TXWwu4DPhkRPwtMy8v9w8BzqV4v/xAZp5Xuf//Az7Xya7/H0Vw/Efgg5k5o/KcxgCrA2TmtyJiIkXQ+pfMPLXB72AMRXA4Dzg0M6+uHDsIuAT4dUSMzcy55f53UgTHTwH7ZOYT5f5RwD+BnTr5fNpzI7BNZt7fps8Hlv36cURsmpmNZq14D7BHZt5RnrMa8Ffg9cDXgeMrbU+kCI6vAd6VmRPLc/oBPwY+SBEof76m5yWpB7DEQtIqpSwP2JQiaN2DIhA+r0HTL7QNjiNiB+AQ4DbghNbgGCAzn6fIWgJ8vHLa24CRwHWtwXHZPoGvAM90ou+7Am8AXgA+VA2Oy2s+kZltg/0lOR4YTPFcr64eKAP8n1B8WHhT5dD/lNtTW4Pjsv0LwEmduPcSZea9bYPjcv8VwJ+ATYBt2jn9R63BcXnODIoMcgIfjIgBABHRBzgZeAU4qjU4Ls+ZW54zEfhImWmWpA7xDUPSKqG1/paiHOAR4FhgOkXWcFyb5glc2uAyB5Tbv2RmS9uDmXknxdf0u1Z2711uf9+g/Tzgwk48jTeW299l5vROnNee1udzcTvHry+3uwJERF9g93LfH9o2LoPqKTX0i/J+/SPizRHxzYj4WVknfT6wbdlk03ZObfS7foCi1GQ1YMdy92uANYGbyg84bc+ZBdwBDF/CvSRpMZZYSFpVXFBuWyjqWe8FLs7MRgHdC9XscMWYcvvNiPjmEu41oPLndcvtk+20fWIJ12mrdTBh24B+WY0pt8/GktdLWbPcrgH0A15cwuwaT1IElMslIralKKUYs4RmQ9rZv6Tf9Q4s/Dtpvfb+sfQFRtakqIeWpKUyQJa0Smg7D/JSzG5nf+u3ZjdQX5DanVqfzwVLbAX/6eqOVJX12X+kCGDPKR/jgRmZmRFxGsU80cu7CmLr83+MouZ5SSYv570k9SAGyJJ6ktZ64b9k5vc6eM6EcrthO8fb29/I0+V2k06csyTPlNc6MTM7EgBOBuYCIyNiYDszO2xQQ7+2KB+3Z+bHGxzfeCnnb0jxDUGj/QDPldvWv8+HOvkBSpKWyBpkST3JVeX2iE6c01rH+462B8pBYm/txLVaB9K9q5yZYWnmltv2khmdej5lzXRrNrnR8zkAGNGRay1Fa4nGYgMYI2I4sP9Szm/Uty0oyitmsHDKu9uAlymmf6uj35IEGCBL6kEy8z8UQeVe5aIciy2MERHbl1OktfoTReZ136gsJFKWEXyNTmRcM/NW4N/AKOBnETG4zb3HlLW7rVozpZu3c8nvAbOA70bEkQ2eS/+IeFtErF/Z/ZNy+7WI2KDSdk3gOx19LkvxGEWt+OvLGUda7zGAotxiacHspyKidSAe5WIfP6QoyTivNfNd1pmfTlHLfHFELJaZjoj1IuI9y/l8JPUwllhI6mmOoViE43+Ad0fEXRSB6FCK+ZFHU8yRfDlAZk6PiA8CF1EsJPJxFi4Usinwc+DDnbj/eyjmG34XcGBE3EAxVd0mFBnSE1lYXnALxZRwb4tiZb3xFIHnuZl5U2Y+FhHvAn4LXBQRjwEPUkx7th7FLA+DKWZ9eKZ8Pr+LiCOAtwMPRMQ/KWYGeX15/VtYONPFMsnMFyLilxS/l7sj4l8UgfzeQG+KxVWOXcIlfg38pzzvZYoFX9YG7qeYWq/qWxTlHO8BHoyIO4HHKQYjbg5sRTFP9v8hSR1kBllSj1LO97sn8GngAYrg8W0UwfF4irmAv9vmnL8C+1Fkf7ehmFd4AsUKbzd18v7PArsAX6UIWvcHDgYGUSxscVml7ezyXldRBM/HUix8sVmbvm1Xnpvl9d5EkaW+lKJc4YE23Xg3xQInzwIHUQTEv6UIkhvN/rEsPk4R7D9OMffz3hQlJjvT/iwVrT5NMYhvQ4oV+RI4G9g7M1+uNszMlsx8b9nuKmAjirKX11IM1vwO8IF6npKkniIaL2IkSZIk9UxmkCVJkqQKA2RJkiSpwgBZkiRJqjBAliRJkioMkCVJkqQKA2RJkiSpwgBZkiRJqjBAliRJkioMkCVJkqQKA2RJkiSpwgBZkiRJqjBAliRJkioMkCVJkqQKA2RJkiSpwgBZkiRJqjBAliRJkioMkCVJkqSK/w9MT8usJkgvYAAAAABJRU5ErkJggg==
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, dpi=150)
",
    "plot_confusion_matrix(clf, X, y, display_labels=["classical", "disco", "reggae"], ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Naive Bayes Classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit and make the prediction
",
    "gnb = GaussianNB()
",
    "y_pred = gnb.fit(X, y).predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.9       , 0.86666667, 0.8       , 0.93333333, 0.63333333,
",
       "       0.76666667, 0.83333333, 0.93333333, 0.73333333, 0.7       ])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 10 fold cross validation score
",
    "scores = cross_val_score(gnb, X, y, cv=10)
",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8099999999999999"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = scores.mean()
",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[93,  3,  4],
",
       "       [ 2, 81, 17],
",
       "       [ 6, 19, 75]], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix of Naive Bayes
",
    "confusion_matrix(y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, dpi=150)
",
    "plot_confusion_matrix(gnb, X, y, display_labels=["classical", "disco", "reggae"], ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1b**
",
    "
",
    "Compute the MFCCs for each recording using the default settings of librosa. Then summarize the entire recording by taking the mean of the MFCCs across the recording as well as the mean and standard deviation across each recording. The resulting configurations will be just the mean (20 features per recording) and the mean and std (40 features per recording). Report on the 10-fold cross-validation classification accuracy and confusion matrix for these two configurations using the linear support vector machine and naive bayes classifier.
",
    "
",
    "(Minimum: 1 point)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_mfcc(folder):
",
    "    mean = []
",
    "    mean_and_std = []
",
    "    
",
    "    for filename in os.listdir(folder):
",
    "        # Load audio file
",
    "        path = os.path.join(folder, filename)
",
    "        y, sr = librosa.load(path)
",
    "        
",
    "        # Compute frame-by-frame mfcc
",
    "        sc = librosa.feature.mfcc(y=y,sr=sr)
",
    "        mean_and_std.append((sc.mean(axis=1),sc.std(axis=1)))
",
    "        mean.append(sc.mean(axis=1))
",
    "        
",
    "    return np.array(mean), np.array(mean_and_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "classical_mean, classical_mean_and_std = compute_mfcc('./a4_resources/GTZAN/classical')
",
    "disco_mean, disco_mean_and_std = compute_mfcc('./a4_resources/GTZAN/disco')
",
    "reggae_mean, reggae_mean_and_std = compute_mfcc('./a4_resources/GTZAN/reggae')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Linear Support Vector Machine:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean = np.concatenate((classical_mean,disco_mean,reggae_mean),axis=0)
",
    "y_mean = np.concatenate((np.array([0]*100),np.array([1]*100),np.array([2]*100)),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 20)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_mean.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC()
",
    "y_pred = clf.fit(X_mean, y_mean).predict(X_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
",
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.63333333, 0.76666667, 0.66666667, 0.86666667, 0.7       ,
",
       "       0.83333333, 0.9       , 0.93333333, 0.73333333, 0.76666667])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = cross_val_score(clf, X_mean, y_mean, cv=10)
",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.78"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy = scores.mean()
",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[73, 27,  0],
",
       "       [ 0, 99,  1],
",
       "       [ 1, 86, 13]], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_mean, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       ""
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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
",
      "text/plain": [
       ""
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,1, dpi=150)
",
    "plot_confusion_matrix(clf, X_mean, y_mean, display_labels=["classical", "disco", "reggae"], ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Std and Mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_mean_and_std = np.concatenate((classical_mean_and_std,disco_mean_and_std,reggae_mean_and_std),axis=0)
",
    "X_mean_and_std = X_mean_and_std.reshape(300,40)
",
    "y_mean_and_std = np.concatenate((np.array([0]*100),np.array([1]*100),np.array([2]*100)),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 40)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_mean_and_std.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\anaconda3\envs\librosa\lib\site-packages\sklearn\svm\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.
",
      "  "the number of iterations.", ConvergenceWarning)
"
     ]
    }
   ],
   "source": [
    "clf = LinearSVC()
",
    "y_pred = clf.fit(X_mean_and_std, y_mean_and_std).predict(X_mean_and_std)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name":

CSC475 / SENG480B Music Retrieval Techniques Assignment 4 Fall 2020 J. Shier October 28, 2020 1 Introduction In this assignment we will explore machine learning topics including super- vised learning...

Answer To: CSC475 / SENG480B Music Retrieval Techniques Assignment 4 Fall 2020 J. Shier October 28, 2020 1...

Answer To This Question Is Available To Download

Related Questions & Answers

Submit New Assignment