Initial commit of John refactor of SNMF

src/diffpy/snmf/main.py

@@ -0,0 +1,32 @@
+import numpy as np
+import snmf_class
+
+X0 = np.loadtxt("input/X0.txt", dtype=float)
+MM = np.loadtxt("input/MM.txt", dtype=float)
+A0 = np.loadtxt("input/A0.txt", dtype=float)
+Y0 = np.loadtxt("input/W0.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, components=2)
+print("Done")
+# 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=" ")