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Signed-off-by: David Rotermund <54365609+davrot@users.noreply.github.com>
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@ -56,5 +56,58 @@ import numpy as np
rng = np.random.default_rng() rng = np.random.default_rng()
print(rng) # -> Generator(PCG64) print(rng) # -> Generator(PCG64)
``` ```
If you don't like it there are other options:
| |
| -------------|
|[MT19937](https://numpy.org/doc/stable/reference/random/bit_generators/mt19937.html)|
|[PCG64](https://numpy.org/doc/stable/reference/random/bit_generators/pcg64.html)|
|[PCG64DXSM](https://numpy.org/doc/stable/reference/random/bit_generators/pcg64dxsm.html)|
|[Philox](https://numpy.org/doc/stable/reference/random/bit_generators/philox.html)|
|[SFC64](https://numpy.org/doc/stable/reference/random/bit_generators/sfc64.html)|
## [Distributions](https://numpy.org/doc/stable/reference/random/generator.html#distributions) (you will use)
The most important ones are in **bold**. If you see a function argument *out*, then you can reuse an existing np array (i.e. [in-place operation](https://numpy.org/doc/stable/reference/random/generator.html#in-place-vs-copy)) as target.
| | |
| ------------- |:-------------:|
|**[integers(low[, high, size, dtype, endpoint])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.integers.html#numpy.random.Generator.integers)**| Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive). |
|**[random([size, dtype, out])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.random.html#numpy.random.Generator.random)** | Return random floats in the half-open interval [0.0, 1.0). |
|**[choice(a[, size, replace, p, axis, shuffle])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.choice.html#numpy.random.Generator.choice)** | Generates a random sample from a given array |
|[bytes(length)](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.bytes.html#numpy.random.Generator.bytes) | Return random bytes. |
|[binomial(n, p[, size])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.binomial.html#numpy.random.Generator.binomial) | Draw samples from a binomial distribution.|
|[multinomial(n, pvals[, size])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.multinomial.html#numpy.random.Generator.multinomial) |Draw samples from a multinomial distribution. |
|[multivariate_normal(mean, cov[, size, ...])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.multivariate_normal.html#numpy.random.Generator.multivariate_normal) | Draw random samples from a multivariate normal distribution.|
|**[normal([loc, scale, size])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.normal.html#numpy.random.Generator.normal)** | Draw random samples from a normal (Gaussian) distribution.|
|**[poisson([lam, size])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.poisson.html#numpy.random.Generator.poisson)** | Draw samples from a Poisson distribution.|
|[standard_normal([size, dtype, out])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.standard_normal.html#numpy.random.Generator.standard_normal) | Draw samples from a standard Normal distribution (mean=0, stdev=1).|
|**[uniform([low, high, size])](https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.uniform.html#numpy.random.Generator.uniform)** |Draw samples from a uniform distribution. |
### random
```python
import numpy as np
rng = np.random.default_rng()
random_values = rng.random(size=(2, 10))
print(random_values)
```
Output:
```
[[0.75309105 0.15751286 0.49454759 0.18204807 0.88459006 0.78685769
0.68525047 0.4000365 0.45317167 0.62412358]
[0.01082224 0.13257961 0.75638974 0.84886965 0.19755022 0.18697649
0.47064409 0.66128207 0.30285691 0.53465021]]
```
### integers
```python
import numpy as np
rng = np.random.default_rng()
random_values = rng.integers(
low=1, high=3, size=(2, 10), dtype=np.uint64, endpoint=True
)
print(random_values)
```
Output:
```
[[2 3 3 2 1 3 1 1 2 2]
[3 3 2 3 3 2 3 3 1 3]]
```