Compressive sensing: numerical generation of RIP matrices
You can't prove RIP through numerical exploration of all possible cases. If you are interested in numerical analysis I suggest to use Coherence instead,  however Coherence is not as strong condition as RIP. Note that RIP is a tool which is used for Mathematical proofs. 
I explain how to check for RIP, in case your are interested.
As I said Coherence is a weaker condition in comparison with RIP, however, it is numerically feasible to check. Coherence of matrix A (which is sensing matrix multiplied in sparsity basis) is defined as below:



where $a_i, a_j$ are columns of matrix A, and $u_A$ is coherence factor. Its computational complexity is of $O(n^2)$ which in many cases is feasible.
For checking coherence, you simply need to compute the given type of correlation between each two different columns of A matrix and among those values chose the max. Based on Welsh Inequality we'll have:



In CS sampling $M&lt;&lt;N$, so the above relation simplifies to $u_A&gt;sqrt(1/M)$, therefore, to satisfy RIP using coherence, number of measurements ($M$) must be of order of $O(K^2)$ where $k$ is number of non-zeros. This number of measurements is so greater than $Klog(N/K)$. 

Another conditions instead of RIP, is stRIP which is much much simpler to prove.

Ref: [A.Amini,"Deterministic Compressed Sensing" PhD Thesis, Sharif University of Technology, Iran, 2011]