def evalExp(data, coeffs):
x,y = data
b, loga = coeffs
a = 2**loga # y = a * 2*(b**x)
y_est = a * pylab.exp2(b*x)
r2 = rSquare(y, y_est)
return y_est, r2
def generatePlots(datasets):
"""
Generate plots to compare the different fit functions for the given
data sets.
Parameters:
datasets - a list of data sets stored as (x, y) tuples, where x and y
are each a list of data points (x or y values)
"""
xdata1, ydata1 = datasets[0]
m = 2
n = 6
funcs = [ polyFit(xdata1, ydata1, 1),
polyFit(xdata1, ydata1, 2),
polyFit(xdata1, ydata1, 4),
expFit(xdata1, ydata1) ]
pylab.figure()
pylab.title("Comparison of fit functions to space cow data sets")
# Generate the polynomial plots
for i in range(0, m*n - 3, 3):
p = funcs[i/3]
for j, data in enumerate(datasets):
x = data[0]
y = data[1]
pylab.subplot(m, n, i+j+1)
ey = [ f(p, xval) for xval in x ]
pylab.plot(x, y, 'bo', x, ey, 'r-')
pylab.title("R2 = %s" % round(rSquare(y, ey), 6))
pylab.xlabel("Time")
pylab.ylabel("# of cows")
# Generate the exponential plots
i = 9
for j, data in enumerate(datasets):
x = data[0]
y = data[1]
pylab.subplot(m, n, i+j+1)
## TWICE-FIXED CODE
b, ln_a = funcs[3] #WRONG: expFit(x, y)
a = pylab.exp2(ln_a)
ey = [a * math.pow(2, b * xval) for xval in x]
pylab.plot(x, y, 'bo', x, ey, 'r-')
pylab.title("R2 = %s" % round(rSquare(y, ey), 6))
pylab.xlabel("Time")
pylab.ylabel("# of cows")
pylab.subplots_adjust(wspace = 0.75, hspace = 0.35)
pylab.show()
def createPlots(data, prtData):
"""
This function saves 4 pylab plots, modeling:
1. Linear
2. Quadratic
3. Quartic
4. Exponential
"""
degree = [1, 2, 4]
iterList = [1, 2, 3]
x = data[0]
y = data[1]
#use for 3 calls to polyFit()
for i in iterList:
#generate unique figure
figure = i + ((prtData - 1) * 4)
#index for degree list
index = i - 1
coef = polyFit(x, y, degree[index])
#reset pylab plotter
pylab.figure(figure)
estimate = pylab.polyval(coef, x)
##print rSquare(y, estimate)
#plots
pylab.scatter(x, y)
pylab.plot(x, estimate)
pylab.savefig(str(figure), format=None)
#single call to expFit()
pylab.figure(4 * prtData)
b, a = expFit(x, y)
a = pylab.exp2(a)
#exponential function
estimate = (a * (2**(b * x)))
##print rSquare(y, estimate)
pylab.scatter(x, y)
pylab.plot(x, estimate)
pylab.savefig(str(4 * prtData), format=None)
def createPlots(data, prtData):
"""
This function saves 4 pylab plots, modeling:
1. Linear
2. Quadratic
3. Quartic
4. Exponential
"""
degree = [1, 2, 4]
iterList = [1,2,3]
x = data[0]
y = data[1]
#use for 3 calls to polyFit()
for i in iterList:
#generate unique figure
figure = i + ((prtData-1) * 4)
#index for degree list
index = i-1
coef = polyFit(x, y, degree[index])
#reset pylab plotter
pylab.figure(figure)
estimate = pylab.polyval(coef, x)
##print rSquare(y, estimate)
#plots
pylab.scatter(x, y)
pylab.plot(x, estimate)
pylab.savefig(str(figure), format=None)
#single call to expFit()
pylab.figure(4 * prtData)
b, a = expFit(x,y)
a = pylab.exp2(a)
#exponential function
estimate = ( a * ( 2 ** (b * x) ) )
##print rSquare(y, estimate)
pylab.scatter(x, y)
pylab.plot(x, estimate)
pylab.savefig(str(4 * prtData), format=None)