Replaced dot with np.dot

kalman_filter/__main__.py

@@ -56,11 +56,11 @@ def main():
 
 
     for i in range(1, n_iterations):
-        measurement = dot(F,x_actual[i-1]) + np.random.normal([0,0], std_dist)
+        measurement = np.dot(F,x_actual[i-1]) + np.random.normal([0,0], std_dist)
 
         f.step(measurement, R, 0)
 
-        x_actual[i]      = dot(F, x_actual[i-1])
+        x_actual[i]      = np.dot(F, x_actual[i-1])
         x_kalman[i]      = f.x
         x_measurement[i] = measurement
 

kalman_filter/kalman_filter.py

@@ -1,5 +1,4 @@
 import numpy as np
-from numpy import dot
 
 
 #
@@ -43,28 +42,28 @@ class KalmanFilter:
     def update(self, z, R):
         # Calculate Kalman Gain
 
-        temp = dot(self.H, dot(self.P,self.H)) + R
+        temp = np.dot(self.H, np.dot(self.P,self.H)) + R
         inv = inv_gen(temp)
-        K = dot(self.P, dot(np.transpose(self.H), inv))
+        K = np.dot(self.P, np.dot(np.transpose(self.H), inv))
 
 
         # Update state
 
-        innovation = z - dot(self.H, self.x)
-        self.x = self.x + dot(K, innovation)
+        innovation = z - np.dot(self.H, self.x)
+        self.x = self.x + np.dot(K, innovation)
 
 
         # Update uncertainty
 
-        KH = dot(K, self.H)
+        KH = np.dot(K, self.H)
         K_complementary = identity_gen(KH) - KH
 
-        self.P = dot(K_complementary, dot(self.P, np.transpose(K_complementary))) \
-                    + dot(K, dot(R, np.transpose(K)))
+        self.P = np.dot(K_complementary, np.dot(self.P, np.transpose(K_complementary))) \
+                    + np.dot(K, np.dot(R, np.transpose(K)))
 
     def predict(self, u):
-        self.x = dot(self.F, self.x) + dot(self.G,u)
-        self.P = dot(self.F, dot(self.P, inv_gen(self.F))) + self.Q
+        self.x = np.dot(self.F, self.x) + np.dot(self.G,u)
+        self.P = np.dot(self.F, np.dot(self.P, inv_gen(self.F))) + self.Q
 
     def step(self, z, R, u):
         self.predict(u)