import numpy as np import os from sklearn.naive_bayes import GaussianNB from helper_package import helper_functions as hf from helper_package import feature_set as fs from helper_package import confusion_matrix_calcs as cm from helper_package import assign_labels as al # raises numpy errors/warnings so they can be caught by try/except np.seterr(all='raise') # allow df console output to display more columns hf.show_more_df() # get DataFrame of stock ticker info from csv file df = hf.fix_column_names(hf.get_ticker_df()) df = al.assign_color_labels(df) # assign color labels df = fs.get_feature_set(df) # add mean and std return columns for DF def nb_predict(df1, df2): """ Gaussian Naive Bayesian classification of labels (colors) :param df1: Training set (DataFrame) :param df2: Prediction set (DataFrame) :return: df2 with predicted label (binary) and color columns """ try: x = df1[['Mean_Return', 'Std_Return']].values
transactions in year 2? 6. Are these results very different from those in year 1 for this value of W? """ import pandas as pd import numpy as np import os import copy import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from helper_package import helper_functions as hf import Assignment4.window_strategy as ws hf.show_more_df() # allow df output to display more rows/columns df = hf.fix_column_names(hf.get_ticker_df()) # get DataFrame of ticket file def create_w_list(w_min, w_max, step=1): """ Create list of w values (used as 'windows' for estimation) :param w_min: Minimum w value :param w_max: maximum w value :param step: step for w values in list :return output_list: list of w values """ output_list = [] # list of w values for i in range(w_min, w_max + 1): output_list.append(i) i += step