Joelfranklin96

import numpy as np # Import the necessary packages.
import pandas as pd 

epsilon = list(range(4)) # Initialisation of epsilon which would store cost function for each value of 'k' after final iteration.

for k in range(2,6): # Number of clusters
    
    cluster = pd.read_csv('/kaggle/input/clustering/k-means clustering.csv') # Read data file into 'cluster'
    
    rows = cluster.shape[0] # 'rows' contains the total number of rows in cluster data.

Joelfranklin96

k = list(range(2,6)) # Range of 'k'
plt.figure(figsize=(20,10)) # Size of figure is adjusted.
plt.xticks(fontsize=20) # Size of number labels on x-axis is adjusted.
plt.yticks(fontsize=20) # Size of number labels on y-axis is adjusted.
plt.plot(k,epsilon,'go--', linewidth=1.5, markersize=4) # Graph is plotted.
plt.xlabel('Value of k',fontsize = 25) # x-axis is labelled.
plt.ylabel('Value of Epsilon',fontsize = 25) # y-axis is labelled.

Joelfranklin96

# Necessary packages are imported.
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import seaborn as sns

# Loading of data set into 'cluster'.
cluster = pd.read_csv('k-means clustering.csv')

k = 3 # Number of clusters

Joelfranklin96

import numpy as np # Import the necessary packages.
import pandas as pd 

epsilon = list(range(4)) # Initialisation of epsilon which would store cost function for each value of 'k' after final iteration.

for k in range(2,6): # Number of clusters
    
    cluster = pd.read_csv('k-means clustering.csv') # Read data file into 'cluster'
    
    rows = cluster.shape[0] # 'rows' contains the total number of rows in cluster data.

Joelfranklin96

from keras.layers import Dropout

# Defining the model

def create_model(learning_rate,dropout_rate):
    model = Sequential()
    model.add(Dense(8,input_dim = 8,kernel_initializer = 'normal',activation = 'relu'))
    model.add(Dropout(dropout_rate))
    model.add(Dense(4,input_dim = 8,kernel_initializer = 'normal',activation = 'relu'))
    model.add(Dropout(dropout_rate))

Joelfranklin96

# Defining the model

def create_model(activation_function,init):
    model = Sequential()
    model.add(Dense(8,input_dim = 8,kernel_initializer = init,activation = activation_function))
    model.add(Dropout(0.1))
    model.add(Dense(4,input_dim = 8,kernel_initializer = init,activation = activation_function))
    model.add(Dropout(0.1))
    model.add(Dense(1,activation = 'sigmoid'))
    

Joelfranklin96

# Defining the model

def create_model(neuron1,neuron2):
    model = Sequential()
    model.add(Dense(neuron1,input_dim = 8,kernel_initializer = 'uniform',activation = 'tanh'))
    model.add(Dropout(0.1))
    model.add(Dense(neuron2,input_dim = neuron1,kernel_initializer = 'uniform',activation = 'tanh'))
    model.add(Dropout(0.1))
    model.add(Dense(1,activation = 'sigmoid'))
    

Joelfranklin96

from sklearn.metrics import classification_report, accuracy_score

# Defining the model

def create_model():
    model = Sequential()
    model.add(Dense(16,input_dim = 8,kernel_initializer = 'uniform',activation = 'tanh'))
    model.add(Dropout(0.1))
    model.add(Dense(4,input_dim = 16,kernel_initializer = 'uniform',activation = 'tanh'))
    model.add(Dropout(0.1))

Joelfranklin96

def create_model(learning_rate,dropout_rate,activation_function,init,neuron1,neuron2):
    model = Sequential()
    model.add(Dense(neuron1,input_dim = 8,kernel_initializer = init,activation = activation_function))
    model.add(Dropout(dropout_rate))
    model.add(Dense(neuron2,input_dim = neuron1,kernel_initializer = init,activation = activation_function))
    model.add(Dropout(dropout_rate))
    model.add(Dense(1,activation = 'sigmoid'))
    
    adam = Adam(lr = learning_rate)
    model.compile(loss = 'binary_crossentropy',optimizer = adam,metrics = ['accuracy'])

Joelfranklin96

# Initialize the dataframe 'numerical_nucleotide' of shape (106,(57*4))

numerical_nucleotide = pd.DataFrame(np.random.randn(106,(57*4)))

# Define the dictionary 'key1'
key1 = {'a' : '1000','c' : '0100','g' : '0010','t' : '0001'}

# Assign values to 'numerical_nucleotide'

for i in range(nucleotide_sequence.shape[0]):