Different outputs with random numbers

fn CovarienceMatrix(borrowed self) -> Matrix:
        let transposed_matrix = self.Transpose()
        var avg_dataset = Matrix(transposed_matrix.height, 1, Float32(0))
        var varience = Matrix(transposed_matrix.height, 1, Float32(0))
        var covarince_matrix = Matrix(transposed_matrix.height, transposed_matrix.height, Float32(0))
        for i in range(transposed_matrix.height):
            @parameter
            fn sum_total[num_elmts: Int](j: Int):
                avg_dataset[i,0] = avg_dataset[i,0] + transposed_matrix.load[num_elmts](i,j).reduce_add[1]()
            vectorize[simd_width, sum_total](transposed_matrix.width)
            avg_dataset[i,0] = avg_dataset[i,0] / transposed_matrix.width
        for i in range(transposed_matrix.height):
            @parameter
            fn normalise_total[num_elmts: Int](j: Int):
                varience[i,0] = varience[i,0] + pow(transposed_matrix.load[num_elmts](i,j) - avg_dataset[i,0], 2).reduce_add[1]()
            vectorize[simd_width, normalise_total](transposed_matrix.width)
            varience[i,0] = sqrt(varience[i,0] / transposed_matrix.width)
        for i in range(transposed_matrix.height):
            for j in range(transposed_matrix.height):
                covarince_matrix[i, j] = varience[i,0] * varience[j,0]
        return covarince_matrix