As I understand the syntax is
In[88]: np.random.seed(seed=0)
In[89]: np.random.rand(5) < 0.8
Out[89]: array([ True, True, True, True, True], dtype=bool)
In[90]: np.random.rand(5) < 0.8
Out[90]: array([ True, True, False, False, True], dtype=bool)
However, when I run the rand(), I get different results. Is there something I am missing with the seed function?
Think of a generator:
def gen(start):
while True:
start += 1
yield start
That will continuously give the next number from the number you insert to the generator. With seeds, it's nearly the same concept. I try to set a variable in which to generate data from, and the position in within that is still saved. Let's put this into practice:
>>> generator = gen(5)
>>> generator.next()
6
>>> generator.next()
7
If you want to restart, you need to restart the generator as well:
>>> generator = gen(5)
>>> generator.next()
6
The same idea with the numpy object. If you want the same results over time, you need to restart the generator, with the same arguments.
>>> np.random.seed(seed=0)
>>> np.random.rand(5) < 0.8
array([ True, True, True, True, True], dtype=bool)
>>> np.random.rand(5) < 0.8
array([ True, True, False, False, True], dtype=bool)
>>> np.random.seed(seed=0) # reset the generator!
>>> np.random.rand(5) < 0.8
array([ True, True, True, True, True], dtype=bool)
seedin-between. It's already deterministic, but that doesn't mean you get the same result every time you callnp.random.np.random.seed()andnp.random.RandomState()