theanets/trainer.py
def reservoir(xs, n, rng):
        '''Select a random sample of n items from xs.'''
        pool = []
        for i, x in enumerate(xs):
            if len(pool) < n:
                pool.append(x / np.linalg.norm(x))
                continue
            j = rng.randint(i + 1)
            if j < n:
                pool[j] = x / np.linalg.norm(x)
        # if the pool still has fewer than n items, pad with distorted random
        # duplicates from the source data.
        L = len(pool)
        S = np.std(pool, axis=0)
        while len(pool) < n:
            x = pool[rng.randint(L)]
            pool.append(x + S * rng.randn(*x.shape))
        return np.array(pool, dtype=pool[0].dtype)
def reservoir(xs, n, rng):
        '''Select a random sample of n items from xs.'''
        pool = []
        for i, x in enumerate(xs):
            if len(pool) < n:
                pool.append(x / np.linalg.norm(x))
                continue
            j = rng.randint(i + 1)
            if j < n:
                pool[j] = x / np.linalg.norm(x)
        # if the pool still has fewer than n items, pad with distorted random
        # duplicates from the source data.
        L = len(pool)
        S = np.std(pool, axis=0)
        while len(pool) < n:
            x = pool[rng.randint(L)]
            pool.append(x + S * rng.randn(*x.shape))
        return np.array(pool, dtype=pool[0].dtype)
Select a random sample of n items from xs.