btcross26

from scipy.special import logsumexp

class BernoulliMixture:
    
    def __init__(self, n_components, max_iter, tol=1e-3):
        self.n_components = n_components
        self.max_iter = max_iter
        self.tol = tol
    
    def fit(self,x):

btcross26

class AttentionPool(nn.Module):
    def __init__(self, embed_dim):
        self.attention = nn.Linear(embed_dim, 1)
    
    def forward(self, x):  # x: (batch, seq_len, embed_dim)
        scores = self.attention(x)  # (batch, seq_len, 1)
        weights = F.softmax(scores, dim=1)  # (batch, seq_len, 1)
        return (weights * x).sum(dim=1)  # (batch, embed_dim)
        
class ReasonCodeTransformer(nn.Module):

btcross26

import pandas as pd
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset, ConceptDriftPreset
from evidently.test_suite import TestSuite
from evidently.tests import DataDriftTest, ConceptDriftTest

# Load your data
reference_data = pd.read_csv('reference_data.csv')  # Your initial training data
current_data = pd.read_csv('current_data.csv')  # Your new production data