diktya.random_search

fmin(f, space_func, n=50, n_jobs='n_cpus', verbose=0)

Minimizes f by using random search.

Parameters:

• f (function) – function to optimize. Gets output of space_func as input.
• space_func (function) – Returns random samples form the search space.
• n (int) – Number of samples to run. Default 50
• n_jobs (int|str) – Number of parallel jobs. Use 'n_cpus'` for same amount as cpus avialable. Default ``'n_cpus'.

Simple Example:

def quadratic_function():
    return (x - 2) ** 2

def space_function():
    return np.random.uniform(-2, 4)

results = fmin(quadratic_function, space_function, n=50)
# sorted by score
print("Min score: {}".format(results[0][0]))