Metropolis-Hastings with two dimensional target distribution

I'm confused in the following situation:

I want to sample by writing code (Java) from the following distribution that is characterized by the mean vectors and covariance matrices:

$$p\left ( \mathbf{x} \right ) = \frac{w_{1}}{2\pi \sqrt{\left | \mathbf{C}_{1} \right |}}e^{-\frac{1}{2}\left ( \mathbf{x}-\mu _{1} \right )^{\top }C_{1}^{-1}\left ( \mathbf{x}-\mu _{1} \right )} + \frac{w_{2}}{2\pi \sqrt{\left | \mathbf{C}_{2} \right |}}e^{-\frac{1}{2}\left ( \mathbf{x}-\mu _{2} \right )^{\top }C_{2}^{-1}\left ( \mathbf{x}-\mu _{2} \right )}$$ where for $$i = 1, 2, \mu_{i} \hspace{35pt} \mathrm{and}\hspace{35pt} \mathbf{C}_{i}$$ are respectively the mean vectors and the covariance matrices. $$(\mathbf{x}-\mu _{i})^{\top }$$ is the row vector corresponding with the column vector $$(\mathbf{x}-\mu _{i})$$

We assume that the first and second mean are the column vectors $$\boldsymbol{\mu }_{1} = (0,0)^{\top } \enspace \mathrm{and }\enspace \boldsymbol{\mu }_{1} = (d,0)^{\top }$$ and the first and second covariance matrices are $$\mathbf{C}_{1} = \begin{pmatrix} \sigma _{1} & 0\\ 0 & \sigma _{2} \end{pmatrix} \enspace \mathrm{and }\enspace \mathbf{C}_{2} = \begin{pmatrix} a & b\\ b & d \end{pmatrix} \enspace \mathrm{respectively}.$$

Furthermore, $$w_{1} = 0.7 ,\thinspace w_{2} = 0.3$$ and $$\mu _{1} = \binom{4}{5}, \mu _{2} = \binom{0.7}{3.5}, \mathbf{C}_{1} = \begin{pmatrix} 1.0 & 0.7\\ 0.7 & 0.1 \end{pmatrix},$$ and $$\mathbf{C}_{2} = \begin{pmatrix} 1.0 & -0.7\\ -0.7 & 1.0 \end{pmatrix}$$

Since I'm using symmetric proposal distributions in the Metropolis-Hastings algorithm, the acceptance probability reduces to where $$\mathbf{x} \enspace \mathrm{and } \enspace \mathbf{x}^{*}$$ are respectively the current state and the proposed new state accepted with probability $$A(\mathbf{x},\mathbf{x}^{*})$$

I want to sample for both the Gaussian distribution and the uniform distribution. The Gaussian distribution is $$q(\mathbf{x^{*}},\mathbf{x}) = \frac{1}{2\pi\sqrt{\left | \mathbf{C} \right |}}e^{-\frac{1}{2}(\mathbf{x^{*}-x})^{\top }\mathbf{C^{-1}}(\mathbf{x^{*}}-\mathbf{x})}$$ where $$\mathbf{C} = \begin{pmatrix} \sigma & 0\\ 0& \sigma \end{pmatrix}$$ The uniform distribution is over the square $$[x_{1} - r, x_{1} + r] \times [x_{2} - r, x_{2} + r]$$ $$q(\mathbf{x^{*}},\mathbf{x}) = \begin{Bmatrix} \frac{1}{4r^{2}} & \mathrm{for} \enspace x_{1}^{*} \in [x_{1} - r, x_{1} + r]\enspace \mathrm{and } \enspace x_{2}^{*} \in [x_{2} - r, x_{2} + r]\\ 0 & \mathrm{otherwise} \end{Bmatrix}$$

I'm stuck in how to implement this in order to be able to do the sampling.

Let $x^{(i)}$ be the current value in the chain. Using the normal proposal distribution as an example, sample $x^* \sim N(x^{(i)}, C)$ and set $x^{(i+1)} = x^*$ with probability $A(x^{(i)},x^*)$ and otherwise set $x^{(i+1)} = x^{(i)}$.