Here are the examples of the python api numpy.trim_zeros taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.
5 Examples
3
Example 1
Project: deer Source File: agent.py
def avgEpisodeVValue(self):
""" Returns the average V value on the episode (on time steps where a non-random action has been taken)
"""
if (len(self._Vs_on_last_episode) == 0):
return -1
if(np.trim_zeros(self._Vs_on_last_episode)!=[]):
return np.average(np.trim_zeros(self._Vs_on_last_episode))
else:
return 0
0
Example 2
Project: pyflux Source File: gasrank.py
def plot_abilities_one_components(self, team_ids, **kwargs):
import matplotlib.pyplot as plt
figsize = kwargs.get('figsize',(15,5))
if self.latent_variables.estimated is False:
raise Exception("No latent variables estimated!")
else:
plt.figure(figsize=figsize)
if type(team_ids) == type([]):
if type(team_ids[0]) == str:
for team_id in team_ids:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[self.team_dict[team_id]],
trim='b'), label=self.team_strings[self.team_dict[team_id]])
else:
for team_id in team_ids:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[team_id],
trim='b'), label=self.team_strings[team_id])
else:
if type(team_ids) == str:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[self.team_dict[team_ids]],
trim='b'), label=self.team_strings[self.team_dict[team_ids]])
else:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[team_ids],
trim='b'), label=self.team_strings[team_ids])
plt.legend()
plt.ylabel("Power")
plt.xlabel("Games")
plt.show()
0
Example 3
Project: pyflux Source File: gasrank.py
def plot_abilities_two_components(self, team_ids, component_id=0, **kwargs):
import matplotlib.pyplot as plt
figsize = kwargs.get('figsize',(15,5))
if component_id == 0:
name_strings = self.team_strings
name_dict = self.team_dict
else:
name_strings = self.team_strings_2
name_dict = self.team_dict_2
if self.latent_variables.estimated is False:
raise Exception("No latent variables estimated!")
else:
plt.figure(figsize=figsize)
if type(team_ids) == type([]):
if type(team_ids[0]) == str:
for team_id in team_ids:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[component_id].T[name_dict[team_id]],
trim='b'), label=name_strings[name_dict[team_id]])
else:
for team_id in team_ids:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[component_id].T[team_id],
trim='b'), label=name_strings[team_id])
else:
if type(team_ids) == str:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[component_id].T[name_dict[team_ids]],
trim='b'), label=name_strings[name_dict[team_ids]])
else:
plt.plot(np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[component_id].T[team_ids],
trim='b'), label=name_strings[team_ids])
plt.legend()
plt.ylabel("Power")
plt.xlabel("Games")
plt.show()
0
Example 4
Project: pyflux Source File: gasrank.py
def predict_one_component(self, team_1, team_2, neutral=False):
"""
Returns team 1's probability of winning
"""
if self.latent_variables.estimated is False:
raise Exception("No latent variables estimated!")
else:
if type(team_1) == str:
team_1_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[self.team_dict[team_1]], trim='b')[-1]
team_2_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[self.team_dict[team_2]], trim='b')[-1]
else:
team_1_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[team_1], trim='b')[-1]
team_2_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values()).T[team_2], trim='b')[-1]
t_z = self.transform_z()
if neutral is False:
return self.link(t_z[0] + team_1_ability - team_2_ability)
else:
return self.link(team_1_ability - team_2_ability)
0
Example 5
Project: pyflux Source File: gasrank.py
def predict_two_components(self, team_1, team_2, team_1b, team_2b, neutral=False):
"""
Returns team 1's probability of winning
"""
if self.latent_variables.estimated is False:
raise Exception("No latent variables estimated!")
else:
if type(team_1) == str:
team_1_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[0].T[self.team_dict[team_1]], trim='b')[-1]
team_2_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[0].T[self.team_dict[team_2]], trim='b')[-1]
team_1_b_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[1].T[self.team_dict[team_1]], trim='b')[-1]
team_2_b_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[1].T[self.team_dict[team_2]], trim='b')[-1]
else:
team_1_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[0].T[team_1], trim='b')[-1]
team_2_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[0].T[team_2], trim='b')[-1]
team_1_b_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[1].T[team_1_b], trim='b')[-1]
team_2_b_ability = np.trim_zeros(self._model_abilities(self.latent_variables.get_z_values())[1].T[team_2_b], trim='b')[-1]
t_z = self.transform_z()
if neutral is False:
return self.link(t_z[0] + team_1_ability - team_2_ability + team_1_b_ability - team_2_b_ability)
else:
return self.link(team_1_ability - team_2_ability + team_1_b_ability - team_2_b_ability)