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    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n\n    my_options      = my_options or {}\n    points          = my_options.get('maxfev', 30)              # by default perform 30 function evaluations\n    num_parameters  = len(x0)                                   # get number of parameters needed for objective function\n\n    flist = [0.]\n    xlist = []\n    t = 0.001\n\n    for i in range(points):\n        x = [random.random() for i in range(num_parameters)]    # initialise parameters randomly from uniform distribution\n        fnew = func(x, my_args)\n        flist.append(fnew)\n        xlist.append(x)\n\n        # SA Algorithm\n        d = flist[i+1] - flist[i]\n        psa = np.exp(-d/t)\n\n        if d < 0.0:\n            flist[i] = np.copy(flist[i+1])\n        else:\n            if psa>np.random.random():\n                flist[i] = np.copy(flist[i+1])\n            else:\n                x = [random.random() for i in range(num_parameters)]\n                \n        t *= 0.9\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n\n    \n    \n\n    # current_func_value = func(x0, my_args)\n\n    # minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x0 }\n    minimizer_output = { 'fun' : fmin, 'nfev' : points, 'nit' : points, 'x' : best_x }\n\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n\n    my_options      = my_options or {}\n    points          = my_options.get('maxfev', 30)              # by default perform 30 function evaluations\n    num_parameters  = len(x0)                                   # get number of parameters needed for objective function\n\n    flist = [0.]\n    xlist = []\n    t = 0.001\n\n    for i in range(points):\n        x = [random.random() for i in range(num_parameters)]    # initialise parameters randomly from uniform distribution\n        fnew = func(x, my_args)\n        flist.append(fnew)\n        xlist.append(x)\n\n        # SA Algorithm\n        d = flist[i+1] - flist[i]\n        psa = np.exp(-d/t)\n\n        if d < 0.0:\n            flist[i] = np.copy(flist[i+1])\n        else:\n            if psa>np.random.random():\n                flist[i] = np.copy(flist[i+1])\n            else:\n                x = [random.random() for i in range(num_parameters)]\n                \n        t *= 0.9\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n\n    \n    \n\n    # current_func_value = func(x0, my_args)\n\n    # minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x0 }\n    minimizer_output = { 'fun' : fmin, 'nfev' : points, 'nit' : points, 'x' : best_x }\n\n    return minimizer_output\n",
    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n\n    my_options      = my_options or {}\n    points          = my_options.get('maxfev', 30)              # by default perform 30 function evaluations\n    num_parameters  = len(x0)                                   # get number of parameters needed for objective function\n\n    flist = [0.]\n    xlist = []\n    t = 0.001\n\n    for i in range(points):\n        x = [random.random() for i in range(num_parameters)]    # initialise parameters randomly from uniform distribution\n        fnew = func(x, my_args)\n        flist.append(fnew)\n        xlist.append(x)\n\n        # SA Algorithm\n        d = flist[i+1] - flist[i]\n        psa = np.exp(-d/t)\n\n        if d < 0.0:\n            flist[i] = np.copy(flist[i+1])\n        else:\n            if psa>np.random.random():\n                flist[i] = np.copy(flist[i+1])\n            else:\n                x = [random.random() for i in range(num_parameters)]\n                \n        t *= 0.9\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n\n    \n    \n\n    # current_func_value = func(x0, my_args)\n\n    # minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x0 }\n    minimizer_output = { 'fun' : fmin, 'nfev' : points, 'nit' : points, 'x' : best_x }\n\n    return minimizer_output\n"
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    "def my_minimizer( func, x0, my_args=(), my_options=None ):\n    \"Your own attempt at writing a stochastic minimizer\"\n\n    my_options      = my_options or {}\n    points          = my_options.get('maxfev', 30)              # by default perform 30 function evaluations\n    num_parameters  = len(x0)                                   # get number of parameters needed for objective function\n\n    flist = [0.]\n    xlist = []\n    t = 0.001\n\n    for i in range(points):\n        x = [random.random() for i in range(num_parameters)]    # initialise parameters randomly from uniform distribution\n        fnew = func(x, my_args)\n        flist.append(fnew)\n        xlist.append(x)\n\n        # SA Algorithm\n        d = flist[i+1] - flist[i]\n        psa = np.exp(-d/t)\n\n        if d < 0.0:\n            flist[i] = np.copy(flist[i+1])\n        else:\n            if psa>np.random.random():\n                flist[i] = np.copy(flist[i+1])\n            else:\n                x = [random.random() for i in range(num_parameters)]\n                \n        t *= 0.9\n\n    min_idx = np.argmin(flist)\n    best_x  = xlist[min_idx]\n    fmin    = flist[min_idx]\n\n    \n    \n\n    # current_func_value = func(x0, my_args)\n\n    # minimizer_output = { 'fun' : current_func_value, 'nfev' : 1, 'nit' : 1, 'x' : x0 }\n    minimizer_output = { 'fun' : fmin, 'nfev' : points, 'nit' : points, 'x' : best_x }\n\n    return minimizer_output\n"
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