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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    x0 = [0.1, -0.15]\n    my_options      = my_options or {}\n    trials = my_options.get('maxfev', 5)\n\n    trial_vals = np.random.random([trials, 2])\n    func_vals = np.array([func(trial_vals[trial, :], my_args) for trial in range(trials)])\n    func_vals = np.array([func_vals]).T\n\n    syn0 = 2 * np.random.random((2, trials)) - 1\n    syn1 = 2 * np.random.random((trials, 1)) - 1\n    for _ in range(60000):\n        l1 = 1 / (1 + np.exp(-(np.dot(trial_vals, syn0))))\n        l2 = 1 / (1 + np.exp(-(np.dot(l1, syn1))))\n        l2_delta = (func_vals - l2) * (l2 * (1 - l2))\n        l1_delta = l2_delta.dot(syn1.T) * (l1 * (1 - l1))\n        syn1 += l1.T.dot(l2_delta)\n        syn0 += trial_vals.T.dot(l1_delta)\n\n    def sigmoid(x, w):\n        return 1 / (1 + np.exp(-np.dot(x, w)))\n\n    def neural_func(x):\n        l1 = 1 / (1 + np.exp(-(np.dot(x, syn0))))\n        l2 = 1 / (1 + np.exp(-(np.dot(l1, syn1))))\n        return l1, l2\n\n    def neural_func_out(x):\n        return neural_func(x)[1]\n\n    def neural_func_gradient(x):\n        l1, l2 = neural_func(x)\n        gradient = (-syn0 * l1 * (1 - l1)) @ (-syn1 * sigmoid(l1, syn1) * (1 - sigmoid(l1, syn1)))\n        return gradient.T[0]\n\n    res = minimize(neural_func_out, x0, method='BFGS', jac=neural_func_gradient,\n                   options={'disp': True})\n\n    minimizer_output = {'fun': res.fun, 'nfev': trials, 'x': res.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    x0 = [0.1, -0.15]\n    my_options      = my_options or {}\n    trials = my_options.get('maxfev', 5)\n\n    trial_vals = np.random.random([trials, 2])\n    func_vals = np.array([func(trial_vals[trial, :], my_args) for trial in range(trials)])\n    func_vals = np.array([func_vals]).T\n\n    syn0 = 2 * np.random.random((2, trials)) - 1\n    syn1 = 2 * np.random.random((trials, 1)) - 1\n    for _ in range(60000):\n        l1 = 1 / (1 + np.exp(-(np.dot(trial_vals, syn0))))\n        l2 = 1 / (1 + np.exp(-(np.dot(l1, syn1))))\n        l2_delta = (func_vals - l2) * (l2 * (1 - l2))\n        l1_delta = l2_delta.dot(syn1.T) * (l1 * (1 - l1))\n        syn1 += l1.T.dot(l2_delta)\n        syn0 += trial_vals.T.dot(l1_delta)\n\n    def sigmoid(x, w):\n        return 1 / (1 + np.exp(-np.dot(x, w)))\n\n    def neural_func(x):\n        l1 = 1 / (1 + np.exp(-(np.dot(x, syn0))))\n        l2 = 1 / (1 + np.exp(-(np.dot(l1, syn1))))\n        return l1, l2\n\n    def neural_func_out(x):\n        return neural_func(x)[1]\n\n    def neural_func_gradient(x):\n        l1, l2 = neural_func(x)\n        gradient = (-syn0 * l1 * (1 - l1)) @ (-syn1 * sigmoid(l1, syn1) * (1 - sigmoid(l1, syn1)))\n        return gradient.T[0]\n\n    res = minimize(neural_func_out, x0, method='BFGS', jac=neural_func_gradient,\n                   options={'disp': True})\n\n    minimizer_output = {'fun': res.fun, 'nfev': trials, 'x': res.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    x0 = [0.1, -0.15]\n    my_options      = my_options or {}\n    trials = my_options.get('maxfev', 5)\n\n    trial_vals = np.random.random([trials, 2])\n    func_vals = np.array([func(trial_vals[trial, :], my_args) for trial in range(trials)])\n    func_vals = np.array([func_vals]).T\n\n    syn0 = 2 * np.random.random((2, trials)) - 1\n    syn1 = 2 * np.random.random((trials, 1)) - 1\n    for _ in range(60000):\n        l1 = 1 / (1 + np.exp(-(np.dot(trial_vals, syn0))))\n        l2 = 1 / (1 + np.exp(-(np.dot(l1, syn1))))\n        l2_delta = (func_vals - l2) * (l2 * (1 - l2))\n        l1_delta = l2_delta.dot(syn1.T) * (l1 * (1 - l1))\n        syn1 += l1.T.dot(l2_delta)\n        syn0 += trial_vals.T.dot(l1_delta)\n\n    def sigmoid(x, w):\n        return 1 / (1 + np.exp(-np.dot(x, w)))\n\n    def neural_func(x):\n        l1 = 1 / (1 + np.exp(-(np.dot(x, syn0))))\n        l2 = 1 / (1 + np.exp(-(np.dot(l1, syn1))))\n        return l1, l2\n\n    def neural_func_out(x):\n        return neural_func(x)[1]\n\n    def neural_func_gradient(x):\n        l1, l2 = neural_func(x)\n        gradient = (-syn0 * l1 * (1 - l1)) @ (-syn1 * sigmoid(l1, syn1) * (1 - sigmoid(l1, syn1)))\n        return gradient.T[0]\n\n    res = minimize(neural_func_out, x0, method='BFGS', jac=neural_func_gradient,\n                   options={'disp': True})\n\n    minimizer_output = {'fun': res.fun, 'nfev': trials, 'x': res.x}\n\n    return minimizer_output\n"
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}