Merge pull request #13 from normal-computing/seed

Change seed to random key for linalg

Author: SamDuffield

tests/test_linalg.py

@@ -8,32 +8,30 @@ def test_linear_system():
     A = jnp.array([[3, 2], [2, 4.0]])
     b = jnp.array([1, 2.0])
 
-    x = thermox.linalg.solve(A, b, num_samples=10000, dt=0.1, burnin=0, seed=0)
+    x = thermox.linalg.solve(A, b, num_samples=10000, dt=0.1, burnin=0)
 
     assert jnp.allclose(A @ x, b, atol=1e-1)
 
 
 def test_inv():
     A = jnp.array([[3, 2], [2, 4.0]])
 
-    A_inv = thermox.linalg.inv(A, num_samples=10000, dt=0.1, burnin=0, seed=0)
+    A_inv = thermox.linalg.inv(A, num_samples=10000, dt=0.1, burnin=0)
 
     assert jnp.allclose(A @ A_inv, jnp.eye(2), atol=1e-1)
 
 
 def test_expnegm():
     A = jnp.array([[3, 2], [2, 4.0]])
 
-    expnegA = thermox.linalg.expnegm(A, num_samples=10000, dt=0.1, burnin=0, seed=0)
+    expnegA = thermox.linalg.expnegm(A, num_samples=10000, dt=0.1, burnin=0)
 
     assert jnp.allclose(expnegA, jax.scipy.linalg.expm(-A), atol=1e-1)
 
 
 def test_expm():
     A = jnp.array([[-0.4, 0.1], [0.5, -0.3]])
 
-    expA = thermox.linalg.expm(
-        A, num_samples=100000, dt=0.1, burnin=0, seed=0, alpha=1.0
-    )
+    expA = thermox.linalg.expm(A, num_samples=100000, dt=0.1, burnin=0, alpha=1.0)
 
     assert jnp.allclose(expA, jax.scipy.linalg.expm(A), atol=1e-1)

thermox/linalg.py

@@ -11,7 +11,7 @@ def solve(
     num_samples: int = 10000,
     dt: float = 1.0,
     burnin: int = 0,
-    seed: int = 0,
+    key: Array = None,
 ) -> Array:
     """
     Obtain the solution of the linear system
@@ -27,12 +27,13 @@ def solve(
         - num_samples: float, number of samples to be collected.
         - dt: float, time step.
         - burnin: burn-in, steps before which samples are not collected.
-        - seed: random seed
+        - key: JAX random key
 
     Returns:
         - approximate solution, x, of the linear system.
     """
-    key = jax.random.PRNGKey(seed)
+    if key is None:
+        key = jax.random.PRNGKey(0)
     ts = jnp.arange(burnin, burnin + num_samples) * dt
     x0 = jnp.zeros_like(b)
     samples = sample_identity_diffusion(key, ts, x0, A, jnp.linalg.solve(A, b))
@@ -44,7 +45,7 @@ def inv(
     num_samples: int = 10000,
     dt: float = 1.0,
     burnin: int = 0,
-    seed: int = 0,
+    key: Array = None,
 ) -> Array:
     """
     Obtain the inverse of a matrix A by
@@ -56,12 +57,13 @@ def inv(
         - num_samples: float, number of samples to be collected.
         - dt: float, time step.
         - burnin: burn-in, steps before which samples are not collected.
-        - seed: random seed
+        - key: JAX random key
 
     Returns:
         - approximate inverse of A.
     """
-    key = jax.random.PRNGKey(seed)
+    if key is None:
+        key = jax.random.PRNGKey(0)
     ts = jnp.arange(burnin, burnin + num_samples) * dt
     b = jnp.zeros(A.shape[0])
     x0 = jnp.zeros_like(b)
@@ -74,7 +76,7 @@ def expnegm(
     num_samples: int = 10000,
     dt: float = 1.0,
     burnin: int = 0,
-    seed: int = 0,
+    key: Array = None,
     alpha: float = 0.0,
 ) -> Array:
     """
@@ -87,18 +89,19 @@ def expnegm(
         - num_samples: float, number of samples to be collected.
         - dt: float, time step.
         - burnin: burn-in, steps before which samples are not collected.
-        - seed: random seed
+        - key: JAX random key
         - alpha: float, regularization parameter to ensure diffusion matrix
             is symmetric positive definite.
 
     Returns:
         - approximate negative matrix exponential, exp(-A).
     """
+    if key is None:
+        key = jax.random.PRNGKey(0)
+
     A_shifted = (A + alpha * jnp.eye(A.shape[0])) / dt
     B = A_shifted + A_shifted.T
 
-    key = jax.random.PRNGKey(seed)
-
     ts = jnp.arange(burnin, burnin + num_samples) * dt
     b = jnp.zeros(A.shape[0])
     x0 = jnp.zeros_like(b)
@@ -111,7 +114,7 @@ def expm(
     num_samples: int = 10000,
     dt: float = 1.0,
     burnin: int = 0,
-    seed: int = 0,
+    key: Array = None,
     alpha: float = 1.0,
 ) -> Array:
     """
@@ -124,14 +127,14 @@ def expm(
         - num_samples: float, number of samples to be collected.
         - dt: float, time step.
         - burnin: burn-in, steps before which samples are not collected.
-        - seed: random seed
+        - key: JAX random key
         - alpha: float, regularization parameter to ensure diffusion matrix
             is symmetric positive definite.
 
     Returns:
         - approximate matrix exponential, exp(A).
     """
-    return expnegm(-A, num_samples, dt, burnin, seed, alpha)
+    return expnegm(-A, num_samples, dt, burnin, key, alpha)
 
 
 def autocovariance(samples: Array) -> Array: