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| import numpy as np | |
| import pandas as pd | |
| from hmmlearn import hmm | |
| def train_and_predict_hidden_markov_model(df): | |
| window_size = 10 | |
| # train_df = df[['point_won', 'point_loss', 'ace', 'winner', 'double_fault', 'unf_err', 'net_point', 'net_point_won', 'break_pt', 'break_pt_won', 'break_pt_miss']] | |
| train_df = df | |
| # "p1_winner", | |
| # "p2_winner", | |
| # "winner_shot_type", | |
| # "p1_double_fault", | |
| # "p2_double_fault", | |
| # "p1_unf_err", | |
| # "p2_unf_err", | |
| # "p1_net_pt_won", | |
| # "p2_net_pt_won", | |
| # "p1_break_pt_won", | |
| # "p2_break_pt_won", | |
| # "rally_count", | |
| # "serve_width", | |
| # "serve_depth", | |
| # "return_depth" | |
| df["observation"] = 0 | |
| # mapping = {} | |
| # counter = 0 | |
| # for i in range(len(train_df)): | |
| # cur_combination = train_df.iloc[i].to_list() | |
| # | |
| # if str(cur_combination) not in mapping.keys(): | |
| # mapping[str(cur_combination)] = counter | |
| # df.loc[i, "observation"] = counter | |
| # counter += 1 | |
| # else: | |
| # df.loc[i, "observation"] = mapping[str(cur_combination)] | |
| observation_list = df["observation"].to_list() | |
| # value_separated_observation_list = [observation_list[i - window_size: i] for i in range(window_size, len(observation_list))] | |
| # value_separated_observation_list = [[0] * window_size] * window_size + value_separated_observation_list | |
| observations = np.array([np.sum(np.array([train_df.iloc[j].to_list() for j in range(i-window_size, i)]).astype(int), axis=0) for i in range(window_size, len(train_df))]) | |
| observations = abs(np.min(observations)) + observations | |
| observations = observations.astype(int) | |
| m_observations = np.concatenate( | |
| (np.array([observations[0].tolist()] * window_size), observations), | |
| axis=0 | |
| ) | |
| df = pd.concat([df, pd.DataFrame({"window_observation": m_observations.tolist()})], axis=1) | |
| hidden_markov_model = hmm.MultinomialHMM(n_components=5, n_iter=50, tol=0.01) | |
| hidden_markov_model.fit(observations) | |
| start_prob = hidden_markov_model.startprob_ | |
| transition_prob = hidden_markov_model.transmat_ | |
| emission_prob = hidden_markov_model.emissionprob_ | |
| neg_log_likelihood, pred = calculate_momentum(df, hidden_markov_model, m_observations) | |
| _, hidden2observation = hidden_markov_model.score_samples(observations) | |
| state_impacts = np.sum(hidden2observation, axis=0) | |
| return state_impacts, neg_log_likelihood, pred, start_prob, transition_prob, emission_prob | |
| state_impacts = np.zeros((num_states, num_obs)) | |
| for t in range(num_obs): | |
| for i in range(num_states): | |
| state_impacts[i, t] = (forward_prob[t, i] * backward_prob[t, i]) / np.sum( | |
| forward_prob[t, :] * backward_prob[t, :]) | |
| return neg_log_likelihood, pred, start_prob, transition_prob, emission_prob | |
| def calculate_momentum(df, hidden_markov_model, m_observations): | |
| # pred_list = [] | |
| # neg_log_likelihood_list = [] | |
| # for i in range(len(df)): | |
| # neg_log_likelihood, pred = hidden_markov_model.decode(np.array([df.loc[i, "window_observation"]])) | |
| # pred_list.append(pred[0]) | |
| # neg_log_likelihood_list.append(neg_log_likelihood) | |
| # | |
| # return pred_list, neg_log_likelihood_list | |
| neg_log_likelihood, pred = hidden_markov_model.decode(m_observations) | |
| return neg_log_likelihood, pred | |