{
  "context": "This criteria evaluates how effectively an engineer uses PyOpenCL's element-wise arithmetic operations on GPU arrays to implement statistical normalization functions. The focus is on proper usage of pyopencl.array operations including addition, subtraction, multiplication, division, and scalar broadcasting.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "Array Creation",
      "description": "Uses pyopencl.array.to_device() to transfer input data to GPU arrays, and properly retrieves results using .get() method",
      "max_score": 15
    },
    {
      "name": "Context and Queue",
      "description": "Creates OpenCL context using pyopencl.create_some_context() or pyopencl.Context, and command queue using pyopencl.CommandQueue for managing GPU operations",
      "max_score": 10
    },
    {
      "name": "Element-wise Subtraction",
      "description": "Uses element-wise subtraction operator (-) on GPU arrays to compute differences (e.g., x - mean, x - min)",
      "max_score": 15
    },
    {
      "name": "Element-wise Division",
      "description": "Uses element-wise division operator (/) on GPU arrays for normalization (e.g., dividing by std_dev or range)",
      "max_score": 15
    },
    {
      "name": "Element-wise Multiplication",
      "description": "Uses element-wise multiplication operator (*) on GPU arrays for scaling operations (e.g., weight1 * data1, multiplying by range factor)",
      "max_score": 15
    },
    {
      "name": "Element-wise Addition",
      "description": "Uses element-wise addition operator (+) on GPU arrays for combining values (e.g., weight1 * data1 + weight2 * data2, adding new_min offset)",
      "max_score": 15
    },
    {
      "name": "Power Operations",
      "description": "Uses element-wise power operator (**) or appropriate method for computing squared deviations (e.g., (x - mean)**2)",
      "max_score": 10
    },
    {
      "name": "Scalar Broadcasting",
      "description": "Correctly uses scalar values in arithmetic operations with GPU arrays (e.g., array - scalar, array * scalar) leveraging automatic broadcasting",
      "max_score": 5
    }
  ]
}