alinazhanguwo

# Encourage lander to use as little fuel as possible
# i.e. 0.85, or 0.32
fuel_conservation = fuel_remaining / total_fuel 

if distance_to_goal is decreasing:
   if speed < threshold:
       if position is on landing pad:
           # Land successfully; give a big reward
           landing_reward = 100
           # Multiply percentage of remaining fuel

alinazhanguwo

import plotly.graph_objects as go

fig = go.Figure(data=go.Scattergeo(
    lat = df['latitude'],
    lon = df['longitude'],
    text = df['_id'],
    marker = dict(
            size = 8,
            opacity = 0.7,
            reversescale = True,

alinazhanguwo

def get_matches_df(sparse_matrix, name_vector, top=100):
    non_zeros = sparse_matrix.nonzero()
    
    sparserows = non_zeros[0]
    sparsecols = non_zeros[1]
    
    if top:
        nr_matches = top
    else:
        nr_matches = sparsecols.size

alinazhanguwo

from scipy.sparse import csr_matrix
!pip install sparse_dot_topn 
import sparse_dot_topn.sparse_dot_topn as ct

def awesome_cossim_top(A, B, ntop, lower_bound=0):
    # force A and B as a CSR matrix.
    # If they have already been CSR, there is no overhead
    A = A.tocsr()
    B = B.tocsr()
    M, _ = A.shape

alinazhanguwo

STOPWORDS = stopwords.words('english')
STOPWORDS = set(STOPWORDS)
    
def text_prepare(text, STOPWORDS):
    """
        text: a string
        
        return: a clean string
    """
    REPLACE_BY_SPACE_RE = re.compile('[\n\"\'/(){}\[\]\|@,;#]')

alinazhanguwo

###

This season reminds me of the day I met you! You are my best colleague. Thank you!

###

We have surely come a long way! Happy Christmas my friend.

###

alinazhanguwo

Christmas Wishes for Colleagues

###
You have worked very hard this year. You really deserve to have a wonderful Christmas. Merry Christmas my colleague.

###
It has always been a pleasure working with you! I wish you a Merry Christmas!

###
It feels great working with someone as knowledgeable as you and yet so humble. I wish you a Merry Christmas!

alinazhanguwo

# top 10 states in America
'''['California',
 'Florida',
 'Georgia',
 'Illinois',
 'Massachusetts',
 'Michigan',
 'New Jersey',
 'New York',
 'Pennsylvania',

alinazhanguwo

fig1, (ax1, ax2) = plt.subplots(1,2, figsize=(12,8), sharey=False)
fig1.tight_layout() 
fig1.subplots_adjust(top=0.86)

sns.set_color_codes("pastel")
sns.barplot(x="Deaths", y="Province/State", data=bar_plot_df, label="num of Deaths", color="b", ax=ax1)

ax1.legend(ncol=2, loc="lower left", frameon=True)
ax1.set(xlim=(0, 33323), ylabel="", xlabel="Num of Deaths by states")
sns.despine(left=True, bottom=True,ax=ax1)

alinazhanguwo

# set up the parameters
n_init = 12
max_iter = 225
tol = 0.0001
random_state = 42
n_jobs = -1

t0 = dt.now()
print("=========  Start training ... ")