feat: preparation

lstm.py

@@ -0,0 +1,58 @@
+import numpy as np
+
+class LSTM:
+    def __init__(self, input_dim, hidden_dim):
+        # Initialize weights and biases
+        self.Wf = np.random.rand(hidden_dim, hidden_dim + input_dim)
+        self.bf = np.random.rand(hidden_dim, 1)
+        
+        self.Wi = np.random.rand(hidden_dim, hidden_dim + input_dim)
+        self.bi = np.random.rand(hidden_dim, 1)
+        
+        self.WC = np.random.rand(hidden_dim, hidden_dim + input_dim)
+        self.bC = np.random.rand(hidden_dim, 1)
+        
+        self.Wo = np.random.rand(hidden_dim, hidden_dim + input_dim)
+        self.bo = np.random.rand(hidden_dim, 1)
+
+    def sigmoid(self, x):
+        return 1 / (1 + np.exp(-x))
+
+    def tanh(self, x):
+        return np.tanh(x)
+
+    def forward(self, x_t, h_prev, C_prev):
+        # Combine previous hidden state and current input
+        combined = np.vstack((h_prev, x_t))
+
+        # Forget gate
+        f_t = self.sigmoid(np.dot(self.Wf, combined) + self.bf)
+
+        # Input gate
+        i_t = self.sigmoid(np.dot(self.Wi, combined) + self.bi)
+        C_tilde = self.tanh(np.dot(self.WC, combined) + self.bC)
+
+        # Cell state
+        C_t = f_t * C_prev + i_t * C_tilde
+
+        # Output gate
+        o_t = self.sigmoid(np.dot(self.Wo, combined) + self.bo)
+        h_t = o_t * self.tanh(C_t)
+
+        return h_t, C_t
+
+# Example usage
+input_dim = 5  # Input feature size
+hidden_dim = 3  # Number of hidden units
+lstm = LSTM(input_dim, hidden_dim)
+
+# Sample inputs
+h_prev = np.zeros((hidden_dim, 1))  # Previous hidden state
+C_prev = np.zeros((hidden_dim, 1))  # Previous cell state
+x_t = np.random.rand(input_dim, 1)   # Current input
+
+# Forward pass
+h_t, C_t = lstm.forward(x_t, h_prev, C_prev)
+
+print("Current hidden state:", h_t)
+print("Current cell state:", C_t)

main.py

@@ -1,58 +1,12 @@
-import numpy as np
+from flask import Flask
 
-class LSTM:
+app = Flask(__name__)
-    def __init__(self, input_dim, hidden_dim):
-        # Initialize weights and biases
-        self.Wf = np.random.rand(hidden_dim, hidden_dim + input_dim)
-        self.bf = np.random.rand(hidden_dim, 1)
 
-        self.Wi = np.random.rand(hidden_dim, hidden_dim + input_dim)
+@app.route('/')
-        self.bi = np.random.rand(hidden_dim, 1)
+def home():
+    return "Hello, World!"
 
-        self.WC = np.random.rand(hidden_dim, hidden_dim + input_dim)
+if __name__ == '__main__':
-        self.bC = np.random.rand(hidden_dim, 1)
+    app.run(debug=True)
 
-        self.Wo = np.random.rand(hidden_dim, hidden_dim + input_dim)
-        self.bo = np.random.rand(hidden_dim, 1)
 
-    def sigmoid(self, x):
-        return 1 / (1 + np.exp(-x))
-
-    def tanh(self, x):
-        return np.tanh(x)
-
-    def forward(self, x_t, h_prev, C_prev):
-        # Combine previous hidden state and current input
-        combined = np.vstack((h_prev, x_t))
-
-        # Forget gate
-        f_t = self.sigmoid(np.dot(self.Wf, combined) + self.bf)
-
-        # Input gate
-        i_t = self.sigmoid(np.dot(self.Wi, combined) + self.bi)
-        C_tilde = self.tanh(np.dot(self.WC, combined) + self.bC)
-
-        # Cell state
-        C_t = f_t * C_prev + i_t * C_tilde
-
-        # Output gate
-        o_t = self.sigmoid(np.dot(self.Wo, combined) + self.bo)
-        h_t = o_t * self.tanh(C_t)
-
-        return h_t, C_t
-
-# Example usage
-input_dim = 5  # Input feature size
-hidden_dim = 3  # Number of hidden units
-lstm = LSTM(input_dim, hidden_dim)
-
-# Sample inputs
-h_prev = np.zeros((hidden_dim, 1))  # Previous hidden state
-C_prev = np.zeros((hidden_dim, 1))  # Previous cell state
-x_t = np.random.rand(input_dim, 1)   # Current input
-
-# Forward pass
-h_t, C_t = lstm.forward(x_t, h_prev, C_prev)
-
-print("Current hidden state:", h_t)
-print("Current cell state:", C_t)

requirement.txt

@@ -0,0 +1,4 @@
+Flask==3.0.3
+numpy==2.1.2
+pymongo
+Flask-PyMongo