In this project, you need to read data from the file, make some expenses on the read dataset using the run you performed in the 1st project, and not write these calculations to the file.  Your project should have 3 C programs like sections. main.c (#include , #include  , #include matrixLib.h , int main()), matrixLib.c(#include "matrixLib.h",/*function bodies*/ ),( "matrixLib.h" .. /*function prototypes */) The initial information of the Iris.csv dataset contains the index information of the Iris flower data. In Line 2 of the file, the following features of the Iris flower are given: Id: Line line number in the file (It will not be taken into account when reading!) SepalLengthCm: The length of the sepal SepalWidthCm: The width of the sepal PetalLengthCm: The length of the petal PetalWidthCm: The width of the petal Genus: 3 different types of iris flower. These species are: Iris-setosa, Iris-versicolor, Iris-virginica In the given Iris.csv version, each record is separated by a comma (,). Commas will be ignored when reading data.

Each line will be properly read from the file, with a structure variable you named IrisDataType. All the data in the file (except the Id column) will be kept in a dynamic array of type IrisDataType. The last column where the iris flower types are given will be labeled as (Iris-setosa=1, Iris-versicolor=2, Iris-virginica=3) with the appropriate data structure you will create and the reading operation will be from this data structure type. The property matrix of 150x4 dimensions is appropriately taken from the IrisDataType dynamic array you created in the main() function, and the following (functions you wrote in the 1st project float *returnVector(int size),float **returnMatrix(int row, int col),void freeMatrix(float *) *mat, int row),float mean(float *vec, int size), float correlation(float *vec, float *vec2, int size),float covariance(float *vec1, float *vec2, int size),float * *matrixMultiplication(float **mat1, float **mat2, int row1, int col1, int row2, int col2),float **matrixTranspose(float **mat, int row, int col), float *rowMeans(float ** mat, int row, int col), float *columnMeans(float **mat, int row, int col), float **covarianceMatrix(float **mat, int row, int col), float determinant(float **mat, Statistical calculations will be made with int row), void printVector(float *vec, int N), void printMatrix(float **mat, int row, int col) functions.

Mean and variance values of SepalLengthCm, SepalWidthCm, PetalLengthCm and PetalWidthCm features, correlation of 3 feature pairs with each other and covariance matrix of feature matrix will be calculated. The accounts you made will be written to the "IrisStatistic.txt" file you created.