JackWillz

JackWillz

Rank	Team	Player	Average Gold Diff @ 10
1st	Team Vitality	Alphari	327
2nd	Rogue	Odoamne	189
3rd	G2 Esports	Broken Blade	102
4th	Excel Esports	Finn	-26
5th	Fnatic	Wunder	-44
6th	SK Gaming	Jenax	-58
7th	Team BDS	Adam	-69
8th	Misfits Gaming 	HiRit	-119
9th	Astralis	WhiteKnight	-142

JackWillz

# An aggregation dictionary allows you to do the mean for some values, and a sum for others. 
# In our case we want to the average of all the columns, but count the number of games per champion
agg_dict = {'LaneType': 'count'}
for col in df.columns[4:]:
    agg_dict[col] = 'mean'
    
avg_champ_df = df.groupby(['Lane', 'Champion']).agg(agg_dict).reset_index()

JackWillz

avg_champ_df = avg_champ_df[avg_champ_df['Count'] >= 5000]
top_champ_df = avg_champ_df[avg_champ_df['Lane'] == 'top']

JackWillz

avg_champ_df = avg_champ_df[avg_champ_df['Count']>=5000]
top_champ_df = avg_champ_df[avg_champ_df['Lane']=='top']

JackWillz

def normalization(row):
    x_min, x_max = np.max(row), np.min(row)
    return [(x - x_min) / (x_max - x_min) for x in row]

list_of_stats = [normalization(list(top_champ_df[x])) for x in stats]
# Initially it starts column-wise, (i.e. each list contains 1 stat across all champs)
XT = np.array(list_of_stats)
# So we transpose it to row-wise (i.e. each list contains all stats for 1 champ)
X = np.transpose(XT)

JackWillz

def normalization(row):
    row = [np.log(x) for x in row]
    x_min, x_max = np.max(row), np.min(row)
    return [(x - x_min) / (x_max - x_min) for x in row]

list_of_stats = [normalization(list(top_champ_df[x])) for x in stats]
XT = np.array(list_of_stats)
X = np.transpose(XT)

JackWillz

def normalization(row):
    row = [np.log(x) for x in row]
    x_min, x_max = np.max(row), np.min(row)
    return [(x - x_min) / (x_max - x_min) for x in row]

list_of_stats = [normalization(list(lane[x])) for x in questions.values()]
XT = np.array(list_of_stats)
X = np.transpose(XT)

JackWillz

def score_likelihood(decrypted_text, perc_dict):
    total_likelihood = 0
    for i in range(len(decrypted_text) - 1):
        pair_likelihood = perc_dict[decrypted_text[i]][decrypted_text[i+1]]
        total_likelihood += pair_likelihood
    return total_likelihood

def shuffle_pair(current_dict):
    a, b = random.sample(current_dict.keys(), 2)
    proposed_dict = current_dict.copy()