Linear correlation of two vectors?
You can use corrcoef to compute the Correlation coefficients as a measure of linear dependence of the random vectors (i.e you transpose your row vectors, the variables are column vectors, and the rows are observations). With the vectors
u = [1, 2, 3, 4];
v1 = [10, 10, 10, 10];
v2 = [0, 1, 2, 3];

You have
&gt;&gt; R = corrcoef([u', v1', v2'])

R =

     1   NaN     1
   NaN   NaN   NaN
     1   NaN     1

There is a perfect match between u and v2 as v2' = u' - 1 showing that the two variables u and v2 are directly correlated. In the results above R is
$$
\mathbf R = \begin{pmatrix}
\frac{\operatorname{cov}(\mathbf u, \mathbf u)}{\sigma_{\mathbf u}^2}&amp; \frac{\operatorname{cov}(\mathbf u, \mathbf v_1)}{\sigma_{\mathbf u}\sigma_{\mathbf v_1}} &amp; \frac{\operatorname{cov}(\mathbf u, \mathbf v_2)}{\sigma_{\mathbf u}\sigma_{\mathbf v_2}}\\
\frac{\operatorname{cov}(\mathbf v_1, \mathbf u)}{\sigma_{\mathbf v_1}\sigma_{\mathbf u}}&amp; \frac{\operatorname{cov}(\mathbf v_1, \mathbf v_1)}{\sigma_{\mathbf v_1}^2}  &amp; \frac{\operatorname{cov}(\mathbf v_1, \mathbf v_2)}{\sigma_{\mathbf v_1}\sigma_{\mathbf v_2}}\\
\frac{\operatorname{cov}(\mathbf v_2, \mathbf u)}{\sigma_{\mathbf v_2}\sigma_{\mathbf u}}&amp; \frac{\operatorname{cov}(\mathbf v_2, \mathbf v_1)}{\sigma_{\mathbf v_2}\sigma_{\mathbf v_1}} &amp; \frac{\operatorname{cov}(\mathbf v_2, \mathbf v_2)}{\sigma_{\mathbf v_2}^2}
\end{pmatrix}
$$
The NaN results between (v1', u1'), (u', v1'), (v1', v1'), (v1', v2') and (v2', v1') is because v1' is constant, see MATLAB documentation on corrcoef, more specifically because
$$\begin{cases}
\operatorname{cov}(\mathbf v_1, \mathbf u) = \operatorname{cov}(\mathbf u, \mathbf v_1) = 0\\
\operatorname{cov}(\mathbf v_1, \mathbf v_2) = \operatorname{cov}(\mathbf v_2, \mathbf v_1) = 0\\ 
\operatorname{cov}(\mathbf v_1, \mathbf v_1) = 0
\end{cases}\quad\text{and}\quad\sigma_{\mathbf v_1} = 0 
$$
In the end, each element of the matrix $\mathbf R$ is the covariance of two vectors divided by the product of their standard deviations.
EDIT:
Now let's say v1 isn't a constant vector and you have the three vectors as
u = [1, 2, 3, 4];
v1 = [11, 12, 11, 12];
v2 = [0, 1, 2, 3];

You then get
R =

    1.0000    0.4472    1.0000
    0.4472    1.0000    0.4472
    1.0000    0.4472    1.0000

Note that in the case here $\sigma_{\mathbf v_1} \neq 0$,  $\operatorname{cov}(\mathbf v_1, \mathbf u) \neq 0$, and $\operatorname{cov}(\mathbf v_1, \mathbf v_2) \neq 0$