def fib_table(output=True, decimals=3):
"""Generate table showing reduced recursive invocations of fibonacci."""
import math
tbl = DataTable([8, 12, 12], ['N', 'FiRec', 'Model'],
output=output,
decimals=decimals)
tbl.format('FiRec', 'd')
def exp_model(n, a, b):
"""Formula for A*N^B ."""
return a * math.pow(n, b)
for n in range(3, 100):
old = numRecursiveImproved[0]
fib_with_lucas(n)
model = exp_model(n, 0.28711343, 2.58031481)
tbl.row([n, (numRecursiveImproved[0] - old), model])
if numpy_error:
pass
else:
import numpy as np
from scipy.optimize import curve_fit
x_arr = np.array(tbl.column(tbl.labels[0]))
y_arr = np.array(tbl.column(tbl.labels[1]))
def np_exp_model(n, a, b):
"""Formula for A*N^B ."""
return a * np.power(n, b)
if output:
[exp_coeffs, _] = curve_fit(np_exp_model, x_arr, y_arr)
print('A*N^B = {:.12f}*N^{:f} '.format(exp_coeffs[0],
exp_coeffs[1]))
return tbl
def test_table(self):
tbl = DataTable([8, 8, 8], ['N', 'Another', 'SquareRoot'],
output=False,
decimals=4)
tbl.format('Another', 'd')
for n in range(2, 10):
tbl.row([n, n, n**0.5])
self.assertEqual(tbl.entry(3, 'Another'), 3)
print('Testing that Table is print to console')
tbl = DataTable([8, 8, 8], ['N', 'Another', 'SquareRoot'], decimals=4)
tbl.format('Another', 'd')
for n in range(2, 10):
tbl.row([n, n, n**0.5])
self.assertEqual(list(range(2, 10)), tbl.column('Another'))
model = tbl.best_model('Another')[0]
if numpy_error:
pass
else:
self.assertEqual(model[0], Model.LINEAR)
self.assertAlmostEqual(model[3], 1.0000, places=5)