diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..75ec3f0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1 @@
+.vscode/*
\ No newline at end of file
diff --git a/main.py b/main.py
index e69de29..4f837bc 100644
--- a/main.py
+++ b/main.py
@@ -0,0 +1,131 @@
+from typing import Tuple
+import numpy as np
+
+class QR:
+    def __init__(self) -> None:
+        pass
+    """
+    Checks if caulcuated matricies fulfill QR decomposition conditions, that is:
+    A = QR , where Q -> Q * Qt = I and R is a upper triangular matrix
+    """
+    def __check_condition(self, Q: np.matrix, R: np.matrix) -> bool:
+        if not np.allclose(R, np.triu(R)):
+            print("R matrix is not upper traingle.")
+            return False
+        I = np.identity(Q.shape[1])
+        comparison = np.equal(np.matmul(np.transpose(Q), Q), I)
+        if not comparison.all():
+            print("Q matrix is not orthogonal.")
+            return False
+        return True
+    """
+    Checks if given matrix is 2d, m >= n and filled with real numbers
+    """
+    def __check_pre_conditions(self, matrix: np.ndarray) -> bool:
+        if not matrix.shape[0] >= matrix.shape[1]:
+            print("Matrix is m is lesser than n.")
+            return False
+        if len(matrix.shape) != 2:
+            print("Matrix is not 2D.")
+            return False
+        if not np.isreal(matrix).all():
+            print("Matrix doesn't containt all real numbers.")
+            return False
+        return True
+
+    """
+    Method that performs Householder Transformation QR, acceptcs 2D, real numbers matrix, that
+    fulfills m >= n condition. Return Q and R matrices.
+    """
+    def perform_householder_QR(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        if(self.__check_pre_conditions(matrix)):
+            return self.__householder_qr(matrix)
+        else:
+            print("Incorrect type.")
+            raise Exception
+
+    def __householder(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        v = matrix / (matrix[0] + np.copysign(np.linalg.norm(matrix), matrix[0]))
+        v[0] = 1
+        tau = 2 / (v.T @ v)
+        return v,tau
+
+    def __householder_qr(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        m,n = matrix.shape
+        R = matrix.copy()
+        Q = np.identity(m)
+
+        for j in range(0, n):
+            v, tau = self.__householder(R[j:, j, np.newaxis])
+
+            H = np.identity(m)
+            H[j:, j:] -= tau * (v @ v.T)
+            R = H @ R
+            Q = H @ Q
+
+        return Q[:n].T, np.triu(R[:n])
+
+    def __givens_rotation(self, a: np.ndarray, b: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        if b == 0:
+            c = 1
+            s = 0
+        else:
+            if np.abs(b) > np.abs(a):
+                r = a / b
+                s = 1 / np.sqrt(1 + r**2)
+                c = s * r
+            else:
+                r = b / a
+                c = 1 / np.sqrt(1 + r**2)
+                s = c * r
+        return c,s
+
+    def __givens_qr(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
+        m, n = matrix.shape
+        R = matrix
+        Q = np.eye(m)
+        G = np.zeros((2,2))
+        for j in range(n):
+            for i in reversed(range(j+1, m)):
+                a, b = R[i-1, j], R[i, j]
+                G = np.asarray([[a, b], [-b, a]]) / np.sqrt(a**2 + b**2)
+                R[i-1:i+1, j] = G @ R[i-1:i+1, j]
+                Q[i-1:i+1, :] = G @ Q[i-1:i+1, :]
+
+        return Q, R
+
+    """
+    Method that performs Givens Rotation QR, acceptcs 2D, real numbers matrix, that
+    fulfills m >= n condition. Return Q and R matrices.
+    """
+    def perform_givens_QR(self, matrix: np.ndarray) ->  Tuple[np.ndarray, np.ndarray]:
+        if(self.__check_pre_conditions(matrix)):
+            return self.__givens_qr(matrix)
+        else:
+            print("Incorrect type.")
+            raise Exception
+
+
+
+"""
+Usage example
+"""
+if __name__ == "__main__":
+    qr = QR()
+    matrix = np.matrix('1 2 4; 5 6 7; 8 9 10')
+    matrix = np.asarray(matrix)
+    print(matrix)
+    Q, R = qr.perform_householder_QR(matrix)
+    print("Householder:")
+    print(Q)
+    print(R)
+    print("Givens")
+    Q, R = qr.perform_givens_QR(matrix)
+    print(Q)
+    print(R)
+    # dla porównania
+    print("Numpy")
+    Q, R = np.linalg.qr(matrix)
+    print(Q)
+    print(R)
+