Menu
In short, the problem I encounter is this:
aa = np.arange(-1., 0.001, 0.01)
aa[-1]
Out[16]: 8.8817841970012523e-16
In reality, this cause a series problem since my simulations doesn't allow positive value inputs.
I can sort of get around by doing:
aa = np.arange(-100, 1, 1)/100.
aa[-1]
Out[21]: 0.0
But this is a pain. Practically you can't do this every time.
This seems like such a basic problem. There's gotta be something I am missing here. By the way, I am using Python 2.7.13.
667
The main difference between the two is that range is a built-in Python class, while arange() is a function that belongs to a third-party library (NumPy). In addition, their purposes are different! Generally, range is more suitable when you need to iterate using the Python for loop.
The essential difference between NumPy linspace and NumPy arange is that linspace enables you to control the precise end value, whereas arange gives you more direct control over the increments between values in the sequence.
This is because the arange() takes much lesser memory than that of the built-in range() function. The range function is considerably slower as it generates a range object just like a generator. It takes more memory space when dealing with large sized Python objects.
Using np.linespace solved it for me:
For example np.linspace(0.5, 0.9, 5) produce [0.5 0.6 0.7 0.8 0.9].
200
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
