Lets test Perlin and PCA on Perlin

test.py

@@ -9,6 +9,7 @@ from columbus import env
 from columbus.observables import Observable, CnnObservable
 
 import colorednoise as cn
+from perlin_noise import PerlinNoise
 
 from priorConditionedAnnealing import pca
 
@@ -33,7 +34,7 @@ def getAvaibleEnvs():
             yield getattr(env, s)
 
 
-def loadConfigDefinedEnv(EnvClass):
+def loadConfigDefinedEnv(EnvClass, alg_name):
     p = input('[Path to config> ')
     with open(p, 'r') as f:
         docs = list([d for d in yaml.safe_load_all(
@@ -57,7 +58,7 @@ def loadConfigDefinedEnv(EnvClass):
             print('Unable to find key "'+key+'"')
             path = input('[Path> ')
     print(cur)
-    return EnvClass(fps=30, **cur)
+    return EnvClass(fps=30, title_appendix=' ['+alg_name+']', **cur)
 
 
 def chooseEnv(alg_name):
@@ -74,7 +75,7 @@ def chooseEnv(alg_name):
             print(
                 '[!] That is a number, but not one that makes sense in this context...')
         if envs[i] in [env.ColumbusConfigDefined]:
-            return loadConfigDefinedEnv(envs[i])
+            return loadConfigDefinedEnv(envs[i], alg_name)
         Env = envs[i]
         return Env(fps=30, agent_draw_path=True, path_decay=1/1024, title_appendix=' ['+alg_name+']', max_steps=30*10, clear_path_on_reset=False)
 
@@ -111,12 +112,62 @@ class Colored_Noise():
             self.beta, (self.dim_a, self.samples), random_state=rand_seed())
 
 
-class PCA_Noise():
+class Perlin_Noise():
-    def __init__(self, dim_a=2, kernel_func='SE_1.41_1', window=64, ssf=-1):
+    def __init__(self, scale=0.05, octaves=1, dim_a=2):
+        self.scale = scale
+        self.octaves = octaves
+        self.dim_a = dim_a
+        self.magic = 3.14159  # Axis offset
+        # We want to genrate samples, that approx ~N(0,1)
+        self.normal_factor = 0.0471
+        self.reset()
+
+    def __call__(self, obs, env):
+        self.index += 1
+        return [self.noise([self.index*self.scale, self.magic*a]) / self.normal_factor
+                for a in range(self.dim_a)]
+
+    def reset(self):
+        self.index = 0
+        self.noise = PerlinNoise(octaves=self.octaves, seed=rand_seed())
+
+
+class Perlin_PCA_Noise():
+    def __init__(self, dim_a=2, kernel_func='SE_1.41_1', window=64, ssf=-1, f_sigma=1):
         self.dim_a = dim_a
         self.kernel_func = kernel_func
         self.window = window
         self.ssf = ssf
+        self.f_sigma = f_sigma
+        self.index = 0
+        self.perlin = Perlin_Noise()
+        self.reset()
+
+    def __call__(self, obs, env):
+        if self.ssf != -1 and self.index % self.ssf == 0:
+            self.traj = [[0]*len(self.traj[0])]
+        traj = th.Tensor(self.traj).unsqueeze(0)
+        eps = th.Tensor(self.perlin(None, None)).unsqueeze(0)
+        sample = self.dist.sample(traj, self.f_sigma, epsilon=eps).squeeze(0)
+        self.traj.append(sample)
+        self.index += 1
+        return sample
+
+    def reset(self):
+        self.dist = pca.PCA_Distribution(
+            action_dim=self.dim_a, par_strength='CONT_DIAG', kernel_func=self.kernel_func, window=self.window)
+        self.dist.proba_distribution(th.Tensor([[0]*2]), th.Tensor([[1]*2]))
+        self.traj = [[0]*self.dim_a]
+        self.perlin.reset()
+
+
+class PCA_Noise():
+    def __init__(self, dim_a=2, kernel_func='SE_1.41_1', window=64, ssf=-1, f_sigma=1):
+        self.dim_a = dim_a
+        self.kernel_func = kernel_func
+        self.window = window
+        self.ssf = ssf
+        self.f_sigma = f_sigma
         self.index = 0
         self.reset()
 
@@ -124,7 +175,7 @@ class PCA_Noise():
         if self.ssf != -1 and self.index % self.ssf == 0:
             self.traj = [[0]*len(self.traj[0])]
         traj = th.Tensor(self.traj).unsqueeze(0)
-        sample = self.dist.sample(traj).squeeze(0)
+        sample = self.dist.sample(traj, self.f_sigma).squeeze(0)
         self.traj.append(sample)
         self.index += 1
         return sample
@@ -172,7 +223,7 @@ def rand_seed():
 
 def choosePlayType():
     options = {'human': human_input, 'PCA': PCA_Noise(),
-               'REX': Colored_Noise(beta=0), 'PINK': Colored_Noise(beta=1), 'BROWN': Colored_Noise(beta=2), 'BETA.5': Colored_Noise(beta=.5), 'PINK_PCA': Colored_PCA_Noise(beta=1)}
+               'REX': Colored_Noise(beta=0), 'PINK': Colored_Noise(beta=1), 'BROWN': Colored_Noise(beta=2), 'BETA.5': Colored_Noise(beta=.5), 'PINK_PCA': Colored_PCA_Noise(beta=1), 'Precise_PCA': PCA_Noise(f_sigma=0.33), 'Perlin': Perlin_Noise(scale=0.05, octaves=1), 'FastPerlin': Perlin_Noise(scale=0.2, octaves=1), 'SlowPerlin': Perlin_Noise(scale=0.0125, octaves=1), 'Perlin_3': Perlin_Noise(scale=0.05, octaves=3), 'Perlin_8': Perlin_Noise(scale=0.05, octaves=8), 'Perlin_PCA': Perlin_PCA_Noise()}
     for i, name in enumerate(options):
         print('['+str(i)+'] '+name)
     while True: