Implement the KDE function which computes i=1 where zi are the data, is a parameter to control the smoothness, and is the kernel density function passed as kernel . def kde (kernel, alpha, x, data): Compute the kernel density estimate for the single query point x. Args: kernel: a kernel function with 3 parameters: alpha, x, data alpha: the smoothing parameter to pass to the kernel x: a single query point (in one dimension) data: a numpy array of data points Returns: The smoothed estimate at the query point x ### BEGIN SOLUTION ### END SOLUTION Assuming you implemented kde correctly, the code below should generate the kde of the log of the income data as before. dfl'trans_inc'-np.log10 (dfl'income' 1) xs = np.linspace ( df ['trans-inc'].min ( ), df ['trans-inc'].max(), 1000) curve - [kde (gaussian kernel, alpha, x, df['trans_inc']) for x in xs] plt.hist (df'trans inc'], normed-True, color-'orange plt.title ( 'World Bank Gross National Income Per Capita') plt.xlabel( 'Log Gross national income per capita, Atlas method: $ 2016 plt.plot (xs, curve,- Implement the KDE function which computes i=1 where zi are the data, is a parameter to control the smoothness, and is the kernel density function passed as kernel . def kde (kernel, alpha, x, data): Compute the kernel density estimate for the single query point x. Args: kernel: a kernel function with 3 parameters: alpha, x, data alpha: the smoothing parameter to pass to the kernel x: a single query point (in one dimension) data: a numpy array of data points Returns: The smoothed estimate at the query point x ### BEGIN SOLUTION ### END SOLUTION Assuming you implemented kde correctly, the code below should generate the kde of the log of the income data as before. dfl'trans_inc'-np.log10 (dfl'income' 1) xs = np.linspace ( df ['trans-inc'].min ( ), df ['trans-inc'].max(), 1000) curve - [kde (gaussian kernel, alpha, x, df['trans_inc']) for x in xs] plt.hist (df'trans inc'], normed-True, color-'orange plt.title ( 'World Bank Gross National Income Per Capita') plt.xlabel( 'Log Gross national income per capita, Atlas method: $ 2016 plt.plot (xs, curve