Gaussian Process kernel tests

kernel.ipynb

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   "source": [
    "import numpy as np\n",
    "from sklearn.gaussian_process.kernels import ConstantKernel, RBF, WhiteKernel, Matern, ExpSineSquared, DotProduct\n",
    "from GPy.kern import Brownian, Cosine, StdPeriodic, RatQuad"
   ]
  },
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   "source": [
    "X = np.array([[1, 0, 1], [2, 1, 3]])\n",
    "Z = np.array([[4, 5, 6], [3, 1, 7], [3, 1, 2]])\n",
    "length_scale = np.array([2, 1, 2])\n",
    "variance = 3"
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    "RBF(length_scale)(X, Z).sum() * 3"
   ]
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     "execution_count": 4,
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    "Matern(length_scale, nu=1/2)(X, Z).sum() * 3"
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     "execution_count": 7,
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   "source": [
    "DotProduct(0)(X, Z).sum() * 3"
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   "source": [
    "(DotProduct(1)(X, Z)**2).sum() * 3"
   ]
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    "WhiteKernel(noise_level=3)(np.array(X), np.array(Z)).sum()"
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   "source": [
    "WhiteKernel(noise_level=3)(np.array(X)).sum()"
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     "execution_count": 11,
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   "source": [
    "Brownian(input_dim=1, variance=3).K(X[:,:1], Z[:,:1]).sum()"
   ]
  },
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     "execution_count": 12,
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   "source": [
    "Cosine(input_dim=3, lengthscale=length_scale, variance=3, ARD=True).K(X, Z).sum()"
   ]
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    "StdPeriodic(input_dim=3, lengthscale=length_scale, variance=3, period=1, ARD2=True, ARD1=False).K(X, Z).sum()"
   ]
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   "source": [
    "RatQuad(input_dim=3, lengthscale=length_scale, variance=3, power=1, ARD=True).K(X, Z).sum()"
   ]
  }
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