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How to use forwards method in autotest
Best Python code snippet using autotest_python
fpl_api.py
Source:fpl_api.py 
1# -*- coding: utf-8 -*-2"""3Created on Wed Oct 29 20:09:42 20144@author: Prathmesh5"""6#Importing Various Python modules 7import pandas as pd8import statsmodels.formula.api as smf9import statsmodels.api as sm10import matplotlib.pyplot as plt11import numpy as np12import requests13from pprint import pprint14import csv15from time import sleep16from sklearn.cross_validation import cross_val_score17from sklearn.linear_model import LinearRegression18from sklearn.cross_validation import train_test_split19from sklearn.metrics import mean_squared_error20#Sample API for one Player21r = requests.get('http://fantasy.premierleague.com/web/api/elements/266/')22top = r.text # unicode text string23top = r.json() # dictionary24pprint(top)25#for loop for scrapping api for all the players26#Reviewers Don't need to Run this Step as I've already got the data in CSV27#This is a time consuming process as this step is accessing web api28s1='http://fantasy.premierleague.com/web/api/elements/'29players = []30for player_link in range(1,624,1):31 link = s1+""+str(player_link)32 r = requests.get(link)33 player =r.json() 34 players.append(player)35 sleep(1)36#Writing the Data from the API into a CSV file37with open('/Users/Prathmesh/Documents/Data-Science-Course/Project/dict_output1.csv', 'wb') as f: # Just use 'w' mode in 3.x38 w = csv.DictWriter(f,player.keys())39 w.writeheader()40 for player in players: 41 w.writerow(player)42# Reading CSV into Pandas DataFrame43# dict_output Refers to Player data from a November Week44players_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/dict_output1.csv',index_col='web_name', na_filter=False)45#Observing the Data for Exploration46players_df.head()47players_df.tail()48players_df.dtypes49players_df.iloc[0,:]50#Some random plotting to determine which attributes to use in the final model51# I did this step for various variables but it didn't reveal much to me.52plt.scatter(players_df.value_form, players_df.points_per_game, alpha=0.3)53plt.scatter(players_df.value_season, players_df.points_per_game, alpha=0.3) 54plt.scatter(forwards_df.bps, forwards_df.points_per_game, alpha=0.3) 55plt.xlabel("BPS")56plt.ylabel("Points Per Game")57#Filtering the data only for Forwards58players_df[players_df.type_name=='Forward'].to_csv('../data/players_updated.csv')59forwards_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/players_updated.csv',index_col='web_name', na_filter=False)60#Exploring the data in the forwards data frame61forwards_df.describe()62forwards_df.head()63forwards_df.tail()64forwards_df.dtypes65forwards_df.points_per_game.describe()66forwards_df.points_per_game.value_counts()67forwards_df.isnull()68#Creating Initial Linear Model for Forwards69forwards_model = smf.ols(formula='event_total ~ selected_by + value_form + value_season + form + ea_index + bps', data=forwards_df).fit()70forwards_model.summary()71# Exploring Multi-collinearity between Variables72columns = ['event_total', 'selected_by', 'value_form', 'value_season', 'form','ea_index','bps']73pd.scatter_matrix(forwards_df[columns])74corr_matrix = np.corrcoef(forwards_df[columns].T)75sm.graphics.plot_corr(corr_matrix, xnames=columns)76# Its obvious from the Correlation Matrix that there is correlation between bps - ea_index & form - value_form77# Hence removing bps & value_from model and exploring78forwards_model = smf.ols(formula='event_total ~ selected_by + form + value_season + ea_index', data=forwards_df).fit()79forwards_model.summary()80#Trying Interaction terms to handle Correlation81# Interaction using * between form & value_form ; form and value_season82interaction_model = smf.ols(formula='event_total ~ selected_by + value_form*form + value_season*form + ea_index', data=forwards_df).fit()83interaction_model.summary()84# Removing players who have played 0 minutes till now85forwards_df[forwards_df.minutes> 0].to_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards.csv')86regular_forwards_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards.csv',index_col='web_name', na_filter=False)87# Running all features models for data set without fringe players88# This inculdes all the possible features, just to study the importance of each feature89all_features_model = smf.ols(formula='event_total ~ selected_by + total_points+ chance_of_playing_this_round + value_form + value_season + form + transfers_out_event+ transfers_in_event + points_per_game + minutes +ea_index + bps', data=regular_forwards_df).fit()90all_features_model.summary()91# Performing Cross Evaluation of the Model92# Creating two interaction terms based on what we learned from multi-collinearity matrix93regular_forwards_df['interaction_term1'] = regular_forwards_df.value_form * regular_forwards_df.form94regular_forwards_df['interaction_term2'] = regular_forwards_df.value_season * regular_forwards_df.form95cols = ['points_per_game','now_cost','selected_by', 'interaction_term1','interaction_term2', 'ea_index']96X = regular_forwards_df[cols]97y = regular_forwards_df.event_total98lm = LinearRegression()99# Also tried RandomForestClassifier but it didn't produce better model as compared to Linear Regression100#rf = RandomForestClassifier(n_estimators=100) 101scores = cross_val_score(lm, X, y, cv=5, scoring='mean_squared_error')102#Calculating Root Mean Squared Error103np.sqrt(-scores)104np.mean(np.sqrt(-scores)) #RMSE for Nov Model 2.0596105# Creating the Same Model for Dec 1 Data Set106players_dec1_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/dec1_players.csv',index_col='web_name', na_filter=False)107players_dec1_df[players_dec1_df.type_name=='Forward'].to_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/forwardplayers_dec1.csv')108forwards_dec1_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/forwardplayers_dec1.csv',index_col='web_name', na_filter=False)109forwards_dec1_df[forwards_dec1_df.minutes> 0].to_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards_dec1.csv')110regular_forwards_dec1_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards_dec1.csv',index_col='web_name', na_filter=False)111regular_forwards_dec1_df['interaction_term1'] = regular_forwards_dec1_df.value_form * regular_forwards_dec1_df.form112regular_forwards_dec1_df['interaction_term2'] = regular_forwards_dec1_df.value_season * regular_forwards_dec1_df.form113cols = ['points_per_game','now_cost','selected_by', 'interaction_term1','interaction_term2', 'ea_index']114X2 = regular_forwards_dec1_df[cols]115y2 = regular_forwards_dec1_df.event_total116lm = LinearRegression()117scores = cross_val_score(lm, X2, y2, cv=5, scoring='mean_squared_error')118np.sqrt(-scores)119np.mean(np.sqrt(-scores)) #RMSE for Dec1 Model 1.6575120# Creating the Same Model for Dec 4 Data Set121players_dec4_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/dec4_players.csv',index_col='web_name', na_filter=False)122players_dec4_df[players_df.type_name=='Forward'].to_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/forwardplayers_dec4.csv')123forwards_dec4_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/forwardplayers_dec4.csv',index_col='web_name', na_filter=False)124forwards_dec4_df[forwards_dec4_df.minutes> 0].to_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards_dec4.csv')125regular_forwards_dec4_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards_dec4.csv',index_col='web_name', na_filter=False)126regular_forwards_dec4_df['interaction_term1'] = regular_forwards_dec4_df.value_form * regular_forwards_dec4_df.form127regular_forwards_dec4_df['interaction_term2'] = regular_forwards_dec4_df.value_season * regular_forwards_dec4_df.form128cols = ['points_per_game','now_cost','selected_by', 'interaction_term1','interaction_term2', 'ea_index']129X3 = regular_forwards_dec4_df[cols]130y3 = regular_forwards_dec4_df.event_total131lm = LinearRegression()132scores = cross_val_score(lm, X3, y3, cv=5, scoring='mean_squared_error')133np.sqrt(-scores)134np.mean(np.sqrt(-scores)) #RMSE for Dec4 Model 2.4378135# Creating the Same Model for Dec 9 Data Set136players_dec9_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/dec9_players.csv',index_col='web_name', na_filter=False)137players_dec9_df[players_df.type_name=='Forward'].to_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/forwardplayers_dec9.csv')138forwards_dec9_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/forwardplayers_dec9.csv',index_col='web_name', na_filter=False)139forwards_dec9_df[forwards_dec9_df.minutes> 0].to_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards_dec9.csv')140regular_forwards_dec9_df = pd.read_csv('/Users/Prathmesh/Documents/Data-Science-Course/Project/regular_forwards_dec9.csv',index_col='web_name', na_filter=False)141regular_forwards_dec9_df['interaction_term1'] = regular_forwards_dec9_df.value_form * regular_forwards_dec9_df.form142regular_forwards_dec9_df['interaction_term2'] = regular_forwards_dec9_df.value_season * regular_forwards_dec9_df.form143cols = ['points_per_game','now_cost','selected_by', 'interaction_term1','interaction_term2', 'ea_index']144X4 = regular_forwards_dec9_df[cols]145y4 = regular_forwards_dec9_df.event_total146lm = LinearRegression()147scores = cross_val_score(lm, X4, y4, cv=5, scoring='mean_squared_error')148np.sqrt(-scores)149np.mean(np.sqrt(-scores)) #RMSE for Dec9 Model 3.14755150# Training the model on the November Data Set & Testing for Dec 1, Dec4 & Dec 9 Data 151cols = ['points_per_game','now_cost','selected_by', 'interaction_term1','interaction_term2', 'ea_index']152X = regular_forwards_df[cols]153y = regular_forwards_df.event_total154lm = LinearRegression()155#rf = RandomForestClassifier(n_estimators=100)156#rf.fit(X,y)157lm.fit(X, y) # fitting the linear regression on Nov values of X & Y158# Testing and Predicting for Dec 1 Data set159preds = lm.predict(X2) #X for Dec 1160# calc RMSE to compare preds vs y for Dec 1161rms = np.sqrt(mean_squared_error(y2, preds))162rms # RMSE for preds for Dec1 data set 2.3746163# Testing and Predicting for Dec 4 Data set164preds = lm.predict(X3) #X for Dec 4165# calc RMSE to compare preds vs y for Dec 4166rms = np.sqrt(mean_squared_error(y3, preds))167rms # RMSE for preds for Dec4 data set 2.8351168# Testing and Predicting for Dec 9 Data set169preds = lm.predict(X4) #X for Dec 9170# calc RMSE to compare preds vs y for Dec 9171rms = np.sqrt(mean_squared_error(y4, preds))...
dirgui.py
Source:dirgui.py
...56 except IndexError:57 pass58 main.robot_stop()59 self.forwards_left()60 def call_forwards(self):61 try:62 self.after_cancel(self._job)63 except IndexError:64 pass65 main.robot_stop()66 self.forwards()67 def call_forwards_right(self):68 try:69 self.after_cancel(self._job)70 except IndexError:71 pass72 main.robot_stop()73 self.forwards_right()74 def call_left(self):75 try:76 self.after_cancel(self._job)77 except IndexError:78 pass79 main.robot_stop()80 self.left()81 def call_right(self):82 try:83 self.after_cancel(self._job)84 except IndexError:85 pass86 main.robot_stop()87 self.right()88 def call_backwards_right(self):89 try:90 self.after_cancel(self._job)91 except IndexError:92 pass93 main.robot_stop()94 self.backwards_right()95 def call_backwards(self):96 try:97 self.after_cancel(self._job)98 except IndexError:99 pass100 main.robot_stop()101 self.backwards()102 def call_backwards_left(self):103 try:104 self.after_cancel(self._job)105 except IndexError:106 pass107 main.robot_stop()108 self.backwards_left()109 def forwards_left(self):110 arduino_data = self._get_arduino()111 if arduino_data is not None:112 compass = arduino_data[3]113 switch = arduino_data[-2]114 main.robot_forwards_left(compass, switch)115 self._job = self.after(64, self.forwards_left)116 def forwards(self):117 arduino_data = self._get_arduino()118 if arduino_data is not None:119 compass = arduino_data[3]120 switch = arduino_data[-2]121 main.robot_forwards(compass, switch)122 self._job = self.after(64, self.forwards)123 def forwards_right(self):124 arduino_data = self._get_arduino()125 if arduino_data is not None:126 compass = arduino_data[3]127 switch = arduino_data[-2]128 main.robot_forwards_right(compass, switch)129 self._job = self.after(64, self.forwards_right)130 def right(self):131 arduino_data = self._get_arduino()132 if arduino_data is not None:133 compass = arduino_data[3]134 switch = arduino_data[-2]135 main.robot_right(compass, switch)...
skip_list.py
Source:skip_list.py
1"""2 An implementation of skip list.3 The list stores positive integers without duplicates.4 跳表çä¸ç§å®ç°æ¹æ³ã5 跳表ä¸å¨åçæ¯æ£æ´æ°ï¼å¹¶ä¸å¨åçæ¯ä¸éå¤çã6 Author: Wenru7"""8from typing import Optional9import random10class ListNode:11 def __init__(self, data: Optional[int] = None):12 self._data = data13 self._forwards = [] # Forward pointers14class SkipList:15 _MAX_LEVEL = 1616 def __init__(self):17 self._level_count = 118 self._head = ListNode()19 self._head._forwards = [None] * type(self)._MAX_LEVEL20 def find(self, value: int) -> Optional[ListNode]:21 p = self._head22 for i in range(self._level_count - 1, -1, -1): # Move down a level23 while p._forwards[i] and p._forwards[i]._data < value:24 p = p._forwards[i] # Move along level25 26 return p._forwards[0] if p._forwards[0] and p._forwards[0]._data == value else None27 def insert(self, value: int):28 level = self._random_level()29 if self._level_count < level: self._level_count = level30 new_node = ListNode(value)31 new_node._forwards = [None] * level32 update = [self._head] * level # update is like a list of prevs33 p = self._head34 for i in range(level - 1, -1, -1):35 while p._forwards[i] and p._forwards[i]._data < value:36 p = p._forwards[i]37 38 update[i] = p # Found a prev39 for i in range(level):40 new_node._forwards[i] = update[i]._forwards[i] # new_node.next = prev.next41 update[i]._forwards[i] = new_node # prev.next = new_node42 43 def delete(self, value):44 update = [None] * self._level_count45 p = self._head46 for i in range(self._level_count - 1, -1, -1):47 while p._forwards[i] and p._forwards[i]._data < value:48 p = p._forwards[i]49 update[i] = p50 51 if p._forwards[0] and p._forwards[0]._data == value:52 for i in range(self._level_count - 1, -1, -1):53 if update[i]._forwards[i] and update[i]._forwards[i]._data == value:54 update[i]._forwards[i] = update[i]._forwards[i]._forwards[i] # Similar to prev.next = prev.next.next55 def _random_level(self, p: float = 0.5) -> int:56 level = 157 while random.random() < p and level < type(self)._MAX_LEVEL:58 level += 159 return level60 def __repr__(self) -> str:61 values = []62 p = self._head63 while p._forwards[0]:64 values.append(str(p._forwards[0]._data))65 p = p._forwards[0]66 return "->".join(values)67if __name__ == "__main__":68 l = SkipList()69 for i in range(10):70 l.insert(i)71 print(l)72 p = l.find(7)73 print(p._data)74 l.delete(3)...
跳表.py
Source:跳表.py
1from typing import Optional2import random3class ListNode:4 def __init__(self, data: Optional[int] = None):5 self._data = data6 self._forwards = [] # Forward pointers7class SkipList:8 _MAX_LEVEL = 169 def __init__(self):10 self._level_count = 111 self._head = ListNode()12 self._head._forwards = [None] * type(self)._MAX_LEVEL13 def find(self, value: int) -> Optional[ListNode]:14 p = self._head15 for i in range(self._level_count - 1, -1, -1): # Move down a level16 while p._forwards[i] and p._forwards[i]._data < value:17 p = p._forwards[i] # Move along level18 return p._forwards[0] if p._forwards[0] and p._forwards[0]._data == value else None19 def insert(self, value: int):20 level = self._random_level()21 if self._level_count < level: self._level_count = level22 new_node = ListNode(value)23 new_node._forwards = [None] * level24 update = [self._head] * level # update is like a list of prevs25 p = self._head26 for i in range(level - 1, -1, -1):27 while p._forwards[i] and p._forwards[i]._data < value:28 p = p._forwards[i]29 update[i] = p # Found a prev30 for i in range(level):31 new_node._forwards[i] = update[i]._forwards[i] # new_node.next = prev.next32 update[i]._forwards[i] = new_node # prev.next = new_node33 def delete(self, value):34 update = [None] * self._level_count35 p = self._head36 for i in range(self._level_count - 1, -1, -1):37 while p._forwards[i] and p._forwards[i]._data < value:38 p = p._forwards[i]39 update[i] = p40 if p._forwards[0] and p._forwards[0]._data == value:41 for i in range(self._level_count - 1, -1, -1):42 if update[i]._forwards[i] and update[i]._forwards[i]._data == value:43 update[i]._forwards[i] = update[i]._forwards[i]._forwards[44 i] # Similar to prev.next = prev.next.next45 def _random_level(self, p: float = 0.5) -> int:46 level = 147 while random.random() < p and level < type(self)._MAX_LEVEL:48 level += 149 return level50 def __repr__(self) -> str:51 values = []52 p = self._head53 while p._forwards[0]:54 values.append(str(p._forwards[0]._data))55 p = p._forwards[0]56 return "->".join(values)57if __name__ == "__main__":58 l = SkipList()59 for i in range(10):60 l.insert(i)61 print(l)62 p = l.find(7)63 print(p._data)64 l.delete(3)...
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