From 98851bbbddb7f18c7ba48546d7f5e27f63595d94 Mon Sep 17 00:00:00 2001
From: m5l14i11 <siarhei.melnik@proton.me>
Date: Tue, 17 Sep 2024 14:57:34 +0300
Subject: [PATCH] upd

---
 .../event_dispatcher/load_balancer.py         |  2 +-
 .../event_dispatcher/pid_controller.py        | 47 +++++++++++++++++++
 2 files changed, 48 insertions(+), 1 deletion(-)
 create mode 100644 infrastructure/event_dispatcher/pid_controller.py

diff --git a/infrastructure/event_dispatcher/load_balancer.py b/infrastructure/event_dispatcher/load_balancer.py
index 3baf69ac..4fe5bc6c 100644
--- a/infrastructure/event_dispatcher/load_balancer.py
+++ b/infrastructure/event_dispatcher/load_balancer.py
@@ -1,6 +1,6 @@
 import numpy as np
 
-from .pid import PID
+from .pid_controller import PID
 
 
 def softmax(x):
diff --git a/infrastructure/event_dispatcher/pid_controller.py b/infrastructure/event_dispatcher/pid_controller.py
new file mode 100644
index 00000000..0278af2f
--- /dev/null
+++ b/infrastructure/event_dispatcher/pid_controller.py
@@ -0,0 +1,47 @@
+import numpy as np
+
+
+class PID:
+    def __init__(
+        self,
+        num_groups: int,
+        kp: float = 0.3,
+        ki: float = 0.6,
+        kd: float = 0.1,
+        learning_rate: float = 0.001,
+        decay_rate: float = 0.99,
+    ):
+        self.kp = np.ones(num_groups) * kp
+        self.ki = np.ones(num_groups) * ki
+        self.kd = np.ones(num_groups) * kd
+
+        self.integral_errors = np.zeros(num_groups)
+        self.previous_errors = np.zeros(num_groups)
+
+        self.learning_rate = learning_rate
+        self.decay_rate = decay_rate
+
+    def update(self, errors: np.ndarray):
+        control_outputs = np.zeros_like(errors)
+
+        for i, error in enumerate(errors):
+            self.integral_errors[i] += error
+            derivative = error - self.previous_errors[i]
+
+            self.kp[i] = np.clip(self.kp[i] + self.learning_rate * error, 0, 1)
+            self.ki[i] = np.clip(
+                self.ki[i] + self.learning_rate * self.integral_errors[i], 0, 1
+            )
+            self.kd[i] = np.clip(self.kd[i] + self.learning_rate * derivative, 0, 1)
+
+            control_outputs[i] = (
+                self.kp[i] * error
+                + self.ki[i] * self.integral_errors[i]
+                + self.kd[i] * derivative
+            )
+
+            self.previous_errors[i] = error
+
+        self.learning_rate *= self.decay_rate
+
+        return control_outputs