Assignment.png __MACOSX/._Assignment.png DigitsTesting.csv...

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Attached is the file with the homework assignment I need help with. This is using Python for statistical learning for digit recognition. The structure of the code is given already and will need to add the statistical learning part into the code for it to do the calculation for PLA.




Assignment.png __MACOSX/._Assignment.png DigitsTesting.csv 9,1,1,1,1,1,0.974,0.7805,0.426,0.308,0.048,0.355,0.891,1,1,1,1,1,1,1,1,0.874,0.206,0,0,0.0045,0.0425,0,0.0345,0.738,1,1,1,1,1,1,0.8935,0.103,0,0.1365,0.589,0.8465,0.893,0.812,0.083,0.122,0.911,1,1,1,1,0.961,0.095,0,0.495,0.964,1,1,1,1,0.695,0,0.3645,1,1,1,1,0.494,0,0.376,1,1,1,1,1,0.701,0.337,0,0.0995,0.999,1,1,0.9905,0.1775,0,0.8435,1,1,1,1,0.896,0.012,0,0,0.2935,0.988,1,1,0.9965,0.083,0.0515,0.9755,1,1,1,0.9155,0.43,0,0,0.349,0.9445,1,1,1,1,0.322,0.103,0.918,1,0.7225,0.463,0.0835,0,0,0.152,0.9405,1,1,1,1,1,0.684,0.0225,0,0,0,0,0.0475,0,0,0.631,1,1,1,1,1,1,1,0.7535,0.2745,0.154,0.154,0.5035,0.6185,0,0.059,0.8975,1,1,1,1,1,1,1,1,1,1,1,1,0.4225,0,0.282,1,1,1,1,1,1,1,1,1,1,1,1,0.9955,0.1485,0,0.5125,1,1,1,1,1,1,1,1,1,1,1,1,0.9165,0.0205,0,0.8145,1,1,1,1,1,1,1,1,1,1,1,1,0.8,0.001,0.0795,0.966,1,1,1,1,1,1,1,1,1,1,1,1,0.712,0,0.134,1,1,1,1,1,1,1,1,1,1,1,1,1,0.954,0.285,0.189,0.9865,1,1,1,1,1 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6,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.7085,0.665,1,1,1,1,1,1,1,1,1,1,1,1,0.9415,0.2765,0.0005,0.1125,1,1,1,1,1,1,1,1,1,1,1,0.7945,0.0985,0,0.199,0.3715,1,1,1,1,1,1,1,1,1,1,0.794,0.069,0.0095,0.425,0.99,1,1,1,1,1,0.9845,0.7185,0.6475,0.6475,0.9695,0.8255,0.0685,0.0175,0.6095,1,1,1,1,1,0.973,0.796,0.256,0,0,0,0.3745,0.331,0,0.473,1,1,1,1,1,0.849,0.186,0,0,0.1635,0.4705,0.055,0.0025,0.0025,0.061,0.907,1,1,1,1,1,0.306,0,0.1445,0.7505,0.963,0.836,0.0185,0.2635,0.0055,0.1125,1,1,1,1,1,1,0.1615,0,0.827,1,1,0.439,0,0.6785,0.015,0.039,0.753,0.997,1,1,1,1,0.28,0.369,0.998,0.9925,0.6615,0.007,0.166,0.977,0.4735,0,0,0.2505,0.481,0.4105,0.6075,0.6075,0.6075,0.6075,0.408,0.1815,0,0.2785,0.8705,1,0.9895,0.4055,0.029,0,0,0,0,0,0,0,0,0.052,0.4115,0.9555,1,1,1,1,0.8615,0.7255,0.5405,0.8055,0.5105,0.707,0.5105,0.591,0.824,0.89,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1 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homework-4lexfffl.zip screen-shot-2021-04-16-at-51814-pm-hvn5f2nq-blurlkgi.png
Answered 7 days AfterApr 15, 2021

Answer To: Assignment.png __MACOSX/._Assignment.png DigitsTesting.csv...

Sandeep Kumar answered on Apr 22 2021
142 Votes
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import random \n",
"\n",
"# Load training data and testing data\n",
 "train = np.genfromtxt('DigitsTraining.csv', delimiter=',')\n",
"test = np.genfromtxt('DigitsTesting.csv', delimiter=',')\n",
"\n",
"\n",
"#Pre-processing data into data labels and data\n",
"X_train = train[:, 1:]\n",
"y_train = train[:, 0]\n",
"X_test = test[:, 1:]\n",
"y_test = test[:, 0]\n",
"\n",
"\n",
"#To plot line y = ax + b\n",
"def line(w, data=None):\n",
" a = - w[1] / w[2]\n",
" b = - w[0] / w[2]\n",
" x_line = np.linspace(0.4, 1, 100)\n",
" if data is not None:\n",
" return a*data + b\n",
" else:\n",
" return a*x_line + b\n",
"\n",
"def symmetry(X):\n",
" X_shapes = (16, 16)\n",
" X = X.reshape(X_shapes)\n",
" \n",
" #######################\n",
" # YOUR CODE GOES HERE #\n",
" #######################\n",
"\n",
"\n",
" return symm\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Prepare Training Data for 7's and 1's \n",
"#Extract the Digit1 and Digit2 from the data\n",
"Digit1 = X_train[y_train == 7]\n",
"y_train_Digit1 = np.ones(len(Digit1))\n",
"Digit2 = X_train[y_train == 1]\n",
"y_train_Digit2 = -np.ones(len(Digit2))\n",
"\n",
"\n",
"symm_Digit2 = [symmetry(im) for im in Digit2]\n",
"symm_Digit1 = [symmetry(im) for im in Digit1]\n",
"\n",
"#Calculate averages and symmetries of the digits in training data\n",
" #######################\n",
" # YOUR CODE GOES HERE #\n",
" #######################\n",
"avg_intensity_Digit1 = \n",
"avg_intensity_Digit2 = \n",
"\n",
"\n",
"#Concatenate the intensities and symmetry\n",
"new_Digit1 = np.c_[avg_intensity_Digit1, symm_Digit1]\n",
"new_Digit2 = np.c_[avg_intensity_Digit2, symm_Digit2]\n",
"\n",
"\n",
"#Stack all the data together and randomize their locations.\n",
"X_new = np.r_[new_Digit1, new_Digit2]\n",
"y_new = np.r_[y_train_Digit1, y_train_Digit2]\n",
"R_num = np.random.randint(X_new.shape[0], size = (X_new.shape[0],))\n",
"X_new = X_new[R_num]\n",
"y_new = y_new[R_num]\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Prepare the test data for 7's and 1's \n",
"X_test_Digit1 = X_test[y_test == 7]\n",
"y_test_Digit1 =...
SOLUTION.PDF
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