Initial commit of John refactor of SNMF

src/diffpy/snmf/main.py

@@ -0,0 +1,31 @@
+import numpy as np
+import snmf_class
+
+X0 = np.loadtxt("inputs/my_X0_4.txt", dtype=float)
+MM = np.loadtxt("inputs/my_MM_4.txt", dtype=float)
+A0 = np.loadtxt("inputs/my_A0_4.txt", dtype=float)
+Y0 = np.loadtxt("inputs/my_W0_4.txt", dtype=float)
+N, M = MM.shape
+
+# Convert to DataFrames for display
+# df_X = pd.DataFrame(X, columns=[f"Comp_{i+1}" for i in range(X.shape[1])])
+# df_Y = pd.DataFrame(Y, columns=[f"Sample_{i+1}" for i in range(Y.shape[1])])
+# df_MM = pd.DataFrame(MM, columns=[f"Sample_{i+1}" for i in range(MM.shape[1])])
+# df_Y0 = pd.DataFrame(Y0, columns=[f"Sample_{i+1}" for i in range(Y0.shape[1])])
+
+# Print the matrices
+"""
+print("Feature Matrix (X):\n", df_X, "\n")
+print("Coefficient Matrix (Y):\n", df_Y, "\n")
+print("Data Matrix (MM):\n", df_MM, "\n")
+print("Initial Guess (Y0):\n", df_Y0, "\n")
+"""
+
+my_model = snmf_class.SNMFOptimizer(MM=MM, Y0=Y0, X0=X0, A=A0)
+print(f"My final guess for X: {my_model.X}")
+print(f"My final guess for Y: {my_model.Y}")
+# print(f"Compare to true X: {X_norm}")
+# print(f"Compare to true Y: {Y_norm}")
+np.savetxt("my_new_X.txt", my_model.X, fmt="%.6g", delimiter=" ")
+np.savetxt("my_new_Y.txt", my_model.Y, fmt="%.6g", delimiter=" ")
+np.savetxt("my_new_A.txt", my_model.A, fmt="%.6g", delimiter=" ")

src/diffpy/snmf/old_main.py

@@ -0,0 +1,62 @@
+import numpy as np
+import pandas as pd
+import snmf
+
+# Define fixed feature matrix (X) with distinct, structured features
+X = np.array(
+    [
+        [10, 0, 0],  # First component dominates first feature
+        [0, 8, 0],  # Second component dominates second feature
+        [0, 0, 6],  # Third component dominates third feature
+        [4, 4, 0],  # Mixed contribution to the fourth feature
+        [3, 2, 5],  # Mixed contribution to the fifth feature
+    ],
+    dtype=float,
+)
+
+# Define fixed coefficient matrix (Y) representing weights
+Y = np.array(
+    [[1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [2, 4, 6, 8, 10, 12, 14, 16, 18, 20], [3, 6, 9, 12, 15, 18, 21, 24, 27, 30]],
+    dtype=float,
+)
+
+# Compute the resulting data matrix M
+MM = np.dot(X, Y)
+
+# Normalize matrices X, Y, and M to be between 0 and 1
+X_norm = (X - X.min()) / (X.max() - X.min())
+Y_norm = (Y - Y.min()) / (Y.max() - Y.min())
+MM_norm = (MM - MM.min()) / (MM.max() - MM.min())
+
+# Generate an initial guess Y0 with slightly perturbed values
+Y0 = np.array(
+    [
+        [1.5, 1.8, 2.9, 3.6, 4.8, 5.7, 7.1, 8.2, 9.4, 10.3],
+        [2.2, 4.1, 5.9, 8.1, 9.8, 11.9, 14.2, 16.5, 18.1, 19.7],
+        [2.7, 5.5, 8.8, 11.5, 14.6, 17.8, 20.5, 23.9, 26.3, 29.2],
+    ],
+    dtype=float,
+)
+
+# Normalize Y0 as well
+Y0_norm = (Y0 - Y0.min()) / (Y0.max() - Y0.min())
+
+# Convert to DataFrames for display
+df_X = pd.DataFrame(X, columns=[f"Comp_{i+1}" for i in range(X.shape[1])])
+df_Y = pd.DataFrame(Y, columns=[f"Sample_{i+1}" for i in range(Y.shape[1])])
+df_MM = pd.DataFrame(MM, columns=[f"Sample_{i+1}" for i in range(MM.shape[1])])
+df_Y0 = pd.DataFrame(Y0, columns=[f"Sample_{i+1}" for i in range(Y0.shape[1])])
+
+# Print the matrices
+"""
+print("Feature Matrix (X):\n", df_X, "\n")
+print("Coefficient Matrix (Y):\n", df_Y, "\n")
+print("Data Matrix (MM):\n", df_MM, "\n")
+print("Initial Guess (Y0):\n", df_Y0, "\n")
+"""
+
+my_model = snmf.SNMFOptimizer(MM_norm, Y0_norm)
+print(f"My final guess for X: {my_model.X}")
+print(f"My final guess for Y: {my_model.Y}")
+print(f"Compare to true X: {X_norm}")
+print(f"Compare to true Y: {Y_norm}")