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# Needs EMBED_SIZE ans SENTENCE_SIZE
# df.text_array is a column with list os word in each cell

w2v_model = Word2Vec(
    sentences=df.text_array, 
    vector_size=EMBED_SIZE, 
    window=5, 
    min_count=1, 
    workers=4, 
    seed=1982,

timotta

# Needs gensim w2v_model trained
# EMBED_SIZE defined
# X should be a Matrix with examples on rows, and word indexes in sequence as columns

class LSTMClassification(nn.Module):
    def __init__(self, embed_vector, hidden_dim=100, dropout=0.5):
        super(LSTMClassification, self).__init__()
        self.n_layers = 1
        self.output_size = 1
        self.hidden_dim = hidden_dim

timotta

def color_the_best(x):
    styles = []
    bigger = max(x)
    for i in x:
        if i == bigger:
            styles.append('background-color: lightgreen')
        else:
            styles.append('')
    return styles
df.style.apply(color_the_best, axis=1)

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rDCG^{k,n} =
\frac {
\sum_{i=1}^{n}\frac{rel_{i}}{log_{2}^{i+1}}
}{
\sum_{i=1}^{k}\frac{rel_{max}}{log_{2}^{i+1}}
}

timotta

{
    "babel-preset-expo": "^5.0.0",
    "babel-preset-react-native": "^5.0.0",
    "jest": "^24.8.0",
    "jest-expo": "^26.0.0",
    "react-test-renderer": "^16.2.0",
    "react": "^16.3.0",
    "react-native": "0.61.5",
    "@babel/core": "7.13.10"
}

timotta

SELECT *, pg_size_pretty(total_bytes) AS total
    , pg_size_pretty(index_bytes) AS INDEX
    , pg_size_pretty(toast_bytes) AS toast
    , pg_size_pretty(table_bytes) AS TABLE
  FROM (
  SELECT *, total_bytes-index_bytes-COALESCE(toast_bytes,0) AS table_bytes FROM (
      SELECT c.oid,nspname AS table_schema, relname AS TABLE_NAME
              , c.reltuples AS row_estimate
              , pg_total_relation_size(c.oid) AS total_bytes
              , pg_indexes_size(c.oid) AS index_bytes