def fitData(fileName):
xVals, yVals = getData(fileName)
xVals = numpy.array(xVals)
yVals = numpy.array(yVals)
xVals = xVals*9.81 #get force
numpy.plot(xVals, yVals, 'bo',
label = 'Measured points')
model = numpy.polyfit(xVals, yVals, 1)
xVals = xVals + [2]
yVals = yVals + []
estYVals = numpy.polyval(model, xVals)
numpy.plot(xVals, estYVals, 'r',
label = 'Linear fit, r**2 = '
+ str(round(rSquared(yVals, estYVals), 5)))
model = numpy.polyfit(xVals, yVals, 2)
estYVals = numpy.polyval(model, xVals)
numpy.plot(xVals, estYVals, 'g--',
label = 'Quadratic fit, r**2 = '
+ str(round(rSquared(yVals, estYVals), 5)))
numpy.title('A Linear Spring')
labelPlot()
numpy.legend(loc = 'best')
def fitData1(fileName):
xVals, yVals = getData(fileName)
xVals = numpy.array(xVals)
yVals = numpy.array(yVals)
xVals = xVals * 9.81 #get force
numpy.plot(xVals, yVals, 'bo', label='Measured points')
labelPlot()
model = numpy.polyfit(xVals, yVals, 1)
estYVals = numpy.polyval(model, xVals)
numpy.plot(xVals,
estYVals,
'r',
label='Linear fit, k = ' + str(round(1 / model[0], 5)))
numpy.legend(loc='best')
def fitData(fileName):
xVals, yVals = getData(fileName)
xVals = numpy.array(xVals)
yVals = numpy.array(yVals)
xVals = xVals * 9.81 #get force
numpy.plot(xVals, yVals, 'bo', label='Measured points')
labelPlot()
a, b = numpy.polyfit(xVals, yVals, 1)
estYVals = a * numpy.array(xVals) + b
print('a =', a, 'b =', b)
numpy.plot(xVals,
estYVals,
'r',
label='Linear fit, k = ' + str(round(1 / a, 5)))
numpy.legend(loc='best')
def genFits(xVals, yVals, degrees):
models = []
for d in degrees:
model = numpy.polyfit(xVals, yVals, d)
models.append(model)
return models
for i in range(len(xVals)):
if i in toTrain:
trainX.append(xVals[i])
trainY.append(yVals[i])
else:
testX.append(xVals[i])
testY.append(yVals[i])
return trainX, trainY, testX, testY
numSubsets = 10
dimensions = (1, 2, 3, 4)
rSquares = {}
for d in dimensions:
rSquares[d] = []
for f in range(numSubsets):
trainX, trainY, testX, testY = splitData(xVals, yVals)
for d in dimensions:
model = numpy.polyfit(trainX, trainY, d)
estYVals = numpy.polyval(model, trainX)
estYVals = numpy.polyval(model, testX)
rSquares[d].append(rSquared(testY, estYVals))
print('Mean R-squares for test data')
for d in dimensions:
mean = round(sum(rSquares[d])/len(rSquares[d]), 4)
sd = round(numpy.std(rSquares[d]), 4)
print('For dimensionality', d, 'mean =', mean,
'Std =', sd)
print(rSquares[1])