diff --git a/priorConditionedAnnealing/kernel.py b/priorConditionedAnnealing/kernel.py
new file mode 100644
index 0000000..c5bdecb
--- /dev/null
+++ b/priorConditionedAnnealing/kernel.py
@@ -0,0 +1,34 @@
+import numpy as np
+import scipy
+
+
+def rbf(l=1.0):
+    return se(sig=1.414, l=l)
+
+
+def se(sig=1.414, l=1.0):
+    def func(xa, xb):
+        sq = scipy.spatial.distance.cdist(xa, xb, 'sqeuclidean') / -2*l
+        return (sig**2)*np.exp(sq)
+    return func
+
+
+def brown(f=0.02, eps=0.00000001):
+    def func(xa, xb):
+        m = []
+        for i in range(xa.shape[0]):
+            l = []
+            for j in range(xb.shape[0]):
+                l.append(min(xa[i][0], xb[j][0]))
+            m.append(l)
+
+        return (np.array(m) + eps)*f
+    return func
+
+
+def pink(sig=1.414):
+    def func(xa, xb):
+        d = scipy.spatial.distance.cdist(xa, xb, 'sqeuclidean')
+        m = (d == 1)*1 + (d == 0)*sig
+        return m
+    return func
diff --git a/priorConditionedAnnealing/pca.py b/priorConditionedAnnealing/pca.py
index 02623de..e3d3348 100644
--- a/priorConditionedAnnealing/pca.py
+++ b/priorConditionedAnnealing/pca.py
@@ -8,7 +8,7 @@ from stable_baselines3.common.distributions import sum_independent_dims
 from torch.distributions import Normal
 import torch.nn.functional as F
 
-from priorConditionedAnnealing import noise
+from priorConditionedAnnealing import noise, kernel
 
 
 class Par_Strength(Enum):
@@ -34,24 +34,15 @@ class EnforcePositiveType(Enum):
         return [nn.Identity(), nn.Softplus(beta=1, threshold=20), th.abs, nn.ReLU(inplace=False), th.log][self.value](x)
 
 
-def rbf(l=1.0):
-    return se(sig=1.0, l=l)
-
-
-def se(sig=1.0, l=1.0):
-    def func(xa, xb):
-        sq = scipy.spatial.distance.cdist(xa, xb, 'sqeuclidean') / -2*l
-        return (sig**2)*np.exp(sq)
-    return func
-
-
 class Avaible_Kernel_Funcs(Enum):
     RBF = 0
     SE = 1
+    BROWN = 2
+    PINK = 3
 
     def get_func(self):
         # stil aaaaaaaa
-        return [rbf, se][self.value]
+        return [kernel.rbf, kernel.se, kernel.brown, kernel.pink][self.value]
 
 
 def cast_to_enum(inp, Class):
@@ -75,7 +66,7 @@ class PCA_Distribution(SB3_Distribution):
         self,
         action_dim: int,
         par_strength: Par_Strength = Par_Strength.CONT_DIAG,
-        kernel_func=rbf(),
+        kernel_func=kernel.rbf(),
         init_std: float = 1,
         cond_noise: float = 0,
         window: int = 64,