diff --git a/SCsub b/SCsub
index 7d7f19f..2d0362f 100644
--- a/SCsub
+++ b/SCsub
@@ -55,6 +55,7 @@ sources = [
     "mlpp/multi_output_layer/multi_output_layer_old.cpp",
     "mlpp/hidden_layer/hidden_layer_old.cpp",
     "mlpp/mlp/mlp_old.cpp",
+    "mlpp/pca/pca_old.cpp",
 
     "test/mlpp_tests.cpp",
 ]
diff --git a/mlpp/pca/pca_old.cpp b/mlpp/pca/pca_old.cpp
new file mode 100644
index 0000000..3b52b03
--- /dev/null
+++ b/mlpp/pca/pca_old.cpp
@@ -0,0 +1,54 @@
+//
+//  PCA.cpp
+//
+//  Created by Marc Melikyan on 10/2/20.
+//
+
+#include "pca_old.h"
+#include "../data/data.h"
+#include "../lin_alg/lin_alg.h"
+
+#include <iostream>
+#include <random>
+
+
+
+MLPPPCAOld::MLPPPCAOld(std::vector<std::vector<real_t>> inputSet, int k) :
+		inputSet(inputSet), k(k) {
+}
+
+std::vector<std::vector<real_t>> MLPPPCAOld::principalComponents() {
+	MLPPLinAlg alg;
+	MLPPData data;
+
+	auto [U, S, Vt] = alg.SVD(alg.cov(inputSet));
+	X_normalized = data.meanCentering(inputSet);
+	U_reduce.resize(U.size());
+	for (int i = 0; i < k; i++) {
+		for (int j = 0; j < U.size(); j++) {
+			U_reduce[j].push_back(U[j][i]);
+		}
+	}
+	Z = alg.matmult(alg.transpose(U_reduce), X_normalized);
+	return Z;
+}
+// Simply tells us the percentage of variance maintained.
+real_t MLPPPCAOld::score() {
+	MLPPLinAlg alg;
+	std::vector<std::vector<real_t>> X_approx = alg.matmult(U_reduce, Z);
+	real_t num, den = 0;
+	for (int i = 0; i < X_normalized.size(); i++) {
+		num += alg.norm_sq(alg.subtraction(X_normalized[i], X_approx[i]));
+	}
+	num /= X_normalized.size();
+	for (int i = 0; i < X_normalized.size(); i++) {
+		den += alg.norm_sq(X_normalized[i]);
+	}
+
+	den /= X_normalized.size();
+	if (den == 0) {
+		den += 1e-10; // For numerical sanity as to not recieve a domain error
+	}
+	return 1 - num / den;
+}
+
diff --git a/mlpp/pca/pca_old.h b/mlpp/pca/pca_old.h
new file mode 100644
index 0000000..03ac4c1
--- /dev/null
+++ b/mlpp/pca/pca_old.h
@@ -0,0 +1,31 @@
+
+#ifndef MLPP_PCA_OLD_H
+#define MLPP_PCA_OLD_H
+
+//
+//  PCA.hpp
+//
+//  Created by Marc Melikyan on 10/2/20.
+//
+
+#include "core/math/math_defs.h"
+
+#include <vector>
+
+
+class MLPPPCAOld {
+public:
+	MLPPPCAOld(std::vector<std::vector<real_t>> inputSet, int k);
+	std::vector<std::vector<real_t>> principalComponents();
+	real_t score();
+
+private:
+	std::vector<std::vector<real_t>> inputSet;
+	std::vector<std::vector<real_t>> X_normalized;
+	std::vector<std::vector<real_t>> U_reduce;
+	std::vector<std::vector<real_t>> Z;
+	int k;
+};
+
+
+#endif /* PCA_hpp */
diff --git a/test/mlpp_tests.cpp b/test/mlpp_tests.cpp
index 998e63f..c606910 100644
--- a/test/mlpp_tests.cpp
+++ b/test/mlpp_tests.cpp
@@ -49,6 +49,7 @@
 
 #include "../mlpp/mlp/mlp_old.h"
 #include "../mlpp/wgan/wgan_old.h"
+#include "../mlpp/pca/pca_old.h"
 
 Vector<real_t> dstd_vec_to_vec(const std::vector<real_t> &in) {
 	Vector<real_t> r;
@@ -732,7 +733,7 @@ void MLPPTests::test_pca_svd_eigenvalues_eigenvectors(bool ui) {
 	std::cout << "PCA" << std::endl;
 
 	// PCA done using Jacobi's method to approximate eigenvalues and eigenvectors.
-	MLPPPCA dr(inputSet, 1); // 1 dimensional representation.
+	MLPPPCAOld dr(inputSet, 1); // 1 dimensional representation.
 	std::cout << std::endl;
 	std::cout << "Dimensionally reduced representation:" << std::endl;
 	alg.printMatrix(dr.principalComponents());