approximating-polynom.py

#!/usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt

x = [10**3, 10**4, 10**5, 10**6]
# y = [18.1e-6, 181e-6, 1.79e-3, 18.2e-3]  # 1 x - 7.744
# y = np.array(x)**2                       # 2 x + 3.553e-15
y = [34.9e-6, 576e-6, 37.2e-3, 4.92]       # 1.726 x - 9.874
z = np.polyfit(np.log10(x), np.log10(y), 1)
p = np.poly1d(z)
print(p)
xp = np.linspace(3, 6, 100)
_ = plt.plot(np.log10(x), np.log10(y), '.', xp, p(xp), '-')
plt.show()

measure_performance_linear_vs_quadratic.py

#!/usr/bin/env python
"""Compare performance linear vs. quadratic algorithm."""


def slices_linear(s, step=10):
    for i in range(0, len(s), step):
        chunk = s[i:i+step]  # O(1)


def slices_quadratic(s, step=10):
    for i in range(0, len(s), step):
        chunk = s[:step]
        s = s[step:]  # O(n)


if __name__ == '__main__':
    # https://gist.github.com/4276624
    from reporttime import get_functions_with_prefix, measure

    funcs = get_functions_with_prefix('slices_')
    for n in range(3, 7):
        measure(funcs, args=["a" * 10**n], comment='10**' + str(n))