Added Rayleigh Perlin

priorConditionedAnnealing/noise.py

@@ -126,6 +126,30 @@ class Perlin_Noise():
         self.index = 0
         self.noise = PerlinNoise(octaves=self.octave)
 
+class P_Perlin_Noise():
+    def __init__(self, known_shape=None, scale=0.1, octave=1):
+        self.known_shape = known_shape
+        self.scale = scale
+        self.octave = octave
+        self.magic = PI  # Axis offset, should be (kinda) irrational
+        # We want to genrate samples, that approx ~N(0,1)
+        self.normal_factor = PI/20
+        self.clear_cache_every = 128
+        self.reset()
+
+    def __call__(self, shape=None):
+        if shape == None:
+            shape = self.known_shape
+        self.index += 1
+        noise = [self.noise([self.index*self.scale, self.magic+(2*a)]) / self.normal_factor
+                 for a in range(np.prod(shape))]
+        if self.index % self.clear_cache_every == 0:
+            self.noise.cache = {}
+        return th.Tensor(noise).view(shape)
+
+    def reset(self):
+        self.index = 0
+        self.noise = PerlinNoise(octaves=self.octave)
 
 class Harmonic_Perlin_Noise():
     def __init__(self, known_shape=None, scale=0.1, octaves=8):
@@ -173,3 +197,27 @@ class Dirty_Perlin_Noise():
     def reset(self):
         self.perlin = Perlin_Noise(known_shape=self.known_shape, scale=self.scale, octave=1)
         self.white = White_Noise(known_shape=self.known_shape)
+
+class Rayleigh_Perlin_Noise():
+    def __init__(self, known_shape=None, sigma=0.1):
+        self.known_shape = known_shape
+        self.sigma = sigma
+        self.magic = PI  # Axis offset, should be (kinda) irrational
+        # We want to genrate samples, that approx ~N(0,1)
+        self.normal_factor = PI/20
+        self.clear_cache_every = 128
+        self.reset()
+
+    def __call__(self, shape=None):
+        assert shape == self.known_shape or (shape[1:] == self.known_shape[1:] and shape[0] <= self.known_shape[0])
+        self.index += 1
+        noise = [self.noise([self.index*self.scales[a%np.prod(self.known_shape[:-1])], self.magic+(2*a)]) / self.normal_factor
+                 for a in range(np.prod(shape))]
+        if self.index % self.clear_cache_every == 0:
+            self.noise.cache = {}
+        return th.Tensor(noise).view(shape)
+
+    def reset(self):
+        self.index = 0
+        self.scales = np.random.rayleigh(scale=self.sigma, size=np.prod(self.known_shape[:-1]))
+        self.noise = PerlinNoise(octaves=1)

priorConditionedAnnealing/pca.py

@@ -55,10 +55,12 @@ class Avaible_Noise_Funcs(Enum):
     DIRTYPERLIN = 5
     SDE = 6
     SHORTPINK = 7
+    P_PERLIN = 8
+    RAYLEIGH_PERLIN = 9
 
     def get_func(self):
         # stil aaaaaaaa
-        return [noise.White_Noise, noise.Pink_Noise, noise.Colored_Noise, noise.Perlin_Noise, noise.Harmonic_Perlin_Noise, noise.Dirty_Perlin_Noise, noise.SDE_Noise, noise.shortPink_Noise][self.value]
+        return [noise.White_Noise, noise.Pink_Noise, noise.Colored_Noise, noise.Perlin_Noise, noise.Harmonic_Perlin_Noise, noise.Dirty_Perlin_Noise, noise.SDE_Noise, noise.shortPink_Noise, noise.P_Perlin_Noise, noise.Rayleigh_Perlin_Noise][self.value]
 
 
 def cast_to_enum(inp, Class):