def q3ii():
dfBoston50, dfBoston75, dfDigits = buildData()
#Boston 50 Data Set
x50 = dfBoston50.iloc[:, 0:13]
y50 = dfBoston50.iloc[:, 14]
# Boston 75 Dat Set
x75 = dfBoston75.iloc[:, 0:13]
y75 = dfBoston75.iloc[:, 14]
# Digits Data Set
xDigits = dfDigits.iloc[:, 0:64]
yDigits = dfDigits.iloc[:, 64]
k = 10
pi = 0.75
method = "LinearSVC"
# Running Linear SVC on Boston 50, Boston 75 and digits data sets
print("Applying LinearSVC on Boston50 Data - Q3 Part2")
my_train_test(method, x50, y50, pi, k)
print("Applying LinearSVC on Boston75 Data - Q3 Part2")
my_train_test(method, x75, y75, pi, k)
print("Applying LinearSVC on Digits Data - Q3 Part2")
my_train_test(method, xDigits, yDigits, pi, k)
# Running SVC on Boston 50, Boston 75 and digits data sets
method = "SVC"
print("Applying SVC on Boston50 Data - Q3 Part2")
my_train_test(method, x50, y50, pi, k)
print("Applying SVC on Boston75 Data - Q3 Part2")
my_train_test(method, x75, y75, pi, k)
print("Applying SVC on Digits Data - Q3 Part2")
my_train_test(method, xDigits, yDigits, pi, k)
# Running Logistic Regression Classifier on Boston 50, Boston 75 and digits data sets
method = "LogisticRegression"
print("Applying Logistic Regression on Boston50 Data - Q3 Part2")
my_train_test(method, x50, y50, pi, k)
print("Applying Logistic Regression on Boston75 Data - Q3 Part2")
my_train_test(method, x75, y75, pi, k)
print("Applying Logistic Regression on Digits Data - Q3 Part2")
my_train_test(method, xDigits, yDigits, pi, k)
def q3i():
dfBoston50, dfBoston75, dfDigits = buildData()
x50 = dfBoston50.iloc[:, 0:13]
y50 = dfBoston50.iloc[:,
14] # Take the target50 column as y and not the actualresponse column
x75 = dfBoston75.iloc[:, 0:13]
y75 = dfBoston75.iloc[:,
14] # Take the target 75 column as y and not the actualresponse column
xDigits = dfDigits.iloc[:, 0:64]
yDigits = dfDigits.iloc[:, 64]
k = 10
method = "LinearSVC"
print("Applying LinearSVC on Boston 50 Data : Q3 Part1")
mycrossval(method, x50, y50, k)
print("Applying LinearSVC on Boston75 Data : Q3 Part1")
mycrossval(method, x75, y75, k)
print("Applying LinearSVC on Digits Data : Q3 Part1")
mycrossval(method, xDigits, yDigits, k)
method = "SVC"
print("Applying SVC on Boston 50 Data : Q3 Part1")
mycrossval(method, x50, y50, k)
print("Applying SVC on Boston75 Data : Q3 Part1")
mycrossval(method, x75, y75, k)
print("Applying SVC on Digits Data : Q3 Part1")
mycrossval(method, xDigits, yDigits, k)
method = "LogisticRegression"
print("Applying LogisticRegression on Boston 50 Data : Q3 Part1")
mycrossval(method, x50, y50, k)
print("Applying LogisticRegression on Boston75 Data : Q3 Part1")
mycrossval(method, x75, y75, k)
print("Applying LogisticRegression on Digits Data : Q3 Part1")
mycrossval(method, xDigits, yDigits, k)
import numpy as np
import pandas as pd
from buildData import buildData
from main import rand_proj, quadproj, mycrossval
print(" Running q4 file..")
dfBoston50, dfBoston75, dfDigits = buildData()
def q4():
d = 32 # the value is given
print("Applying Feature Engineering on Digits Data")
x = dfDigits.iloc[:, 0:64]
y = dfDigits.iloc[:, 64]
G = rand_proj(x, d)
x1 = np.dot(x.values, G)
x2 = quadproj(x)
k = 10 # Given k= 10
print("Applying Linear SVC on Digits Data with X1: Q4")
mycrossval("LinearSVC", pd.DataFrame(x1), y, k)
print("Applying Linear SVC on Digits Data with X2: Q4")
mycrossval("LinearSVC", pd.DataFrame(x2), y, k)
print("Applying SVC on Digits Data with X1: Q4")
mycrossval("SVC", pd.DataFrame(x1), y, k)
print("Applying SVC on Digits Data with X2: Q4")
mycrossval("SVC", pd.DataFrame(x2), y, k)
from learnTask import NNNModel
from learnTask import NUM_NEURONS
from learnTask import NOISE_LEVEL
from learnTask import DEFAULT_NOISE_LEVEL
from buildData import buildData
from buildData import INPUT_NOISE
from buildData import DEFAULT_INPUT_NOISE
#%%
for MODEL_IDS in [-1]:
USE_TEST_CASES = False
USE_MODEL = MODEL_IDS #-1 for latest model
inputData, targetData, classes = buildData(USE_TEST_CASES)
experimentName = os.path.dirname(os.path.abspath(__file__))
rootDir = '{}/../'.format(os.path.dirname(os.path.abspath(__file__)))
experimentDir = '{}/'.format(experimentName)
experimentScript = '{}learnTask.py'.format(experimentDir)
experimentDataScript = '{}buildData.py'.format(experimentDir)
#experimentParameters = '{}parameters.pkl'.format(experimentDir)
#
#if os.path.exists(experimentParameters):
# file = open(experimentParameters, 'rb')
# parameters = pickle.load(file)
# file.close()
# rootDir = 'F:\Dropbox\ConservationOfAgentDynamics\WorkingMemoryTask/'
experimentDir = '{}Experiments/{}/'.format(rootDir, experimentName)
if copyDir == '':
copyDir = '{}/'.format(experimentDir)
isOriginalRun = True
experimentScript = '{}learnTask.py'.format(experimentDir)
experimentDataScript = '{}buildData.py'.format(experimentDir)
experimentSimulation = '{}simulateTask.py'.format(experimentDir)
# experimentParameters = '{}parameters.pkl'.format(experimentDir)
print(' Loading data...')
inputData, targetData, classes = buildData(1)
#trainingParams = loadMatlabData('{}pythonParameters.mat'.format(modelDirectory))
#trainingData = loadMatlabData(trainingParams['dataFile'].format(modelDirectory))
if not os.path.isdir(experimentDir):
os.makedirs(experimentDir)
# Build data sequences
print(' Initializing sequences...')
print(' Building sequences...')
data = WeightedSequences()
if len(inputData.shape) == 3: # Trial data
data.initTrials(inputData, targetData, classes)