vectorizer = CountVectorizer(stop_words="english",binary=True,lowercase=False,vocabulary={'Jscript','.Net','TypeScript','NodeJS','Angular','Mongo','CSS','Python','PHP','Photoshop','Oracle','Linux','C++',"Java",'TeamCity','Frontend','Backend','Full stack', 'UI Design', 'Web','Integration','Database design','UX'})
X = vectorizer.fit_transform(corpus)
feature_names = vectorizer.get_feature_names()


vectorizer = CountVectorizer(stop_words="english",binary=True,lowercase=False,vocabulary={'Jscript','.Net','TypeScript','SQL', 'NodeJS','Angular','Mongo','CSS','Python','PHP','Photoshop','Oracle','Linux','C++',"Java",'TeamCity','Frontend','Backend','Full stack', 'UI Design', 'Web','Integration','Database design','UX'})
X = vectorizer.fit_transform(corpus)
feature_names = vectorizer.get_feature_names()
X = X.toarray()


vectorizer = CountVectorizer(stop_words="english",binary=True,lowercase=False,vocabulary={'Jscript','.Net','TypeScript','NodeJS','Angular','Mongo','CSS','Python','PHP','Photoshop','Oracle','Linux','C++',"Java",'TeamCity','Frontend','Backend','Full stack', 'UI Design', 'Web','Integration','Database design','UX'})
X = vectorizer.fit_transform(corpus)
feature_names = vectorizer.get_feature_names()
X = X.toarray()


slopes = []
for col in df1.columns:
    df2 = df1[~np.isnan(df1[col])]
    df3 = df2[['Time', col]]
    npMatrix = np.matrix(df3)
    X, Y = npMatrix[:,0], npMatrix[:,1]
    slope = LinearRegression().fit(X,Y)
    m = slope.coef_[0]
    slopes.append(m)


df = pd.read_csv('titanic.csv')
le = LabelEncoder()
df['Sex'] = le.fit_transform(df['Sex'])
transformed_df = df


# create a LabelEncoder object
le = LabelEncoder()

# fit the encoder to the 'Sex' column of the dataframe
le.fit(df['Sex'])

# transform the 'Sex' column using the fitted encoder
transformed_df = df.copy()
transformed_df['Sex'] = le.transform(df['Sex'])


    le = LabelEncoder()
    df['Sex'] = le.fit_transform(df['Sex'])
    return df
    ### END SOLUTION


import numpy as np
import pandas as pd
from sklearn import linear_model
import statsmodels.api as sm
X_train, y_train, X_test, y_test = load_data()
assert type(X_train) == np.ndarray
assert type(y_train) == np.ndarray
assert type(X_test) == np.ndarray
assert type(y_test) == np.ndarray
ElasticNet = linear_model.ElasticNet() # create a lasso instance
ElasticNet.fit(X_train, y_train) # fit data
# print(lasso.coef_)
# print (lasso.intercept_) # print out the coefficients
print ("R^2 for training set:"),
print (ElasticNet.score(X_train, y_train))

print ('-'*50)

print ("R^2 for test set:"),
print (ElasticNet.score(X_test, y_test))


# Create a MinMaxScaler object
scaler = MinMaxScaler()

# Fit the scaler to the data
scaler.fit(np_array)

# Transform the data
transformed = scaler.transform(np_array)


    scaler = MinMaxScaler()
    scaler.fit(a)
    new_a = scaler.transform(a)
    ### END SOLUTION
    return new_a


close_buy1 = close[:-1]
m5 = ma_50[:-1]
m10 = ma_100[:-1]
ma20 = ma_200[:-1]
b = np.concatenate([close_buy1, m5, m10, ma20], axis=1)
predict = clf.predict(b)


new_X = np.array(X).astype(np.float)
clf.fit(new_X, ['2', '3'])


new_X = np.array(X).astype(float)
clf.fit(new_X, ['2', '3'])


new_X = np.array(X).astype(np.float)
clf.fit(new_X, ['4', '5'])


logReg = LogisticRegression()
logReg.fit(X, y)
predict = logReg.predict(X)


logReg = LogisticRegression()
logReg.fit(X[:None],y)
predict = logReg.predict(X[:None])


train_size = 0.2
train_dataframe, test_dataframe = train_test_split(features_dataframe, train_size=train_size)
train_dataframe = train_dataframe.sort(["date"])
test_dataframe = test_dataframe.sort(["date"])


train_size = 0.8
train_dataframe, test_dataframe = train_test_split(features_dataframe, train_size=train_size)
train_dataframe = train_dataframe.sort(["date"])
test_dataframe = test_dataframe.sort(["date"])


    train_size = 0.2
    train_dataframe, test_dataframe = train_test_split(features_dataframe, train_size=train_size)
    train_dataframe = train_dataframe.sort(["date"])
    test_dataframe = test_dataframe.sort(["date"])
    return train_dataframe, test_dataframe
    ### END SOLUTION


cols = df.columns[2:4]
df[cols + '_scale'] = scaler.fit_transform(df[cols])


cols = myData.columns[2:4]
myData['new_' + cols] = scaler.fit_transform(myData[cols])


count = CountVectorizer(lowercase = False)
vocabulary = count.fit_transform([words])
feature_names = count.get_feature_names()


count = CountVectorizer(lowercase = False)
vocabulary = count.fit_transform([words])
feature_names = count.get_feature_names_out()


# get the results from the GridSearchCV object
results = GridSearch_fitted.cv_results_
# convert the results to a pandas dataframe
df = pd.DataFrame(results)
# sort the dataframe by mean_fit_time
df = df.sort_values('mean_fit_time')
# print the dataframe
print(df)


import pickle
# Save the model in the file named "sklearn_model"
with open('sklearn_model', 'wb') as file:
    pickle.dump(fitted_model, file)


import numpy as np
import pandas as pd
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
df = load_data()
tfidf = TfidfVectorizer()
tfidf_matrix = tfidf.fit_transform(df['description'])
cosine_similarity_matrix = sklearn.metrics.pairwise.cosine_similarity(tfidf_matrix, tfidf_matrix)


optim = torch.optim.SGD(..., lr=0.01)
# Define a function to update the learning rate
def update_learning_rate(optimizer, new_lr):
    for param_group in optimizer.param_groups:
        param_group['lr'] = new_lr
# Define a function to check if the loss has increased
def check_loss_increase(loss, previous_loss):
    if loss > previous_loss:
        return True
    else:
        return False
# Define a function to update the learning rate if the loss has increased
def update_lr_if_loss_increased(optimizer, previous_loss):
    if check_loss_increase(loss, previous_loss):
        update_learning_rate(optimizer, 0.001)
# Define a function to train the model
def train_model(optimizer, model, loss_fn, train_loader, val_loader, epochs):
    previous_loss = float('inf')
    for epoch in range(epochs):
        # Training loop
        for inputs, targets in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = loss_fn(outputs, targets)
            loss.backward()
            optimizer.step()
            # Update the previous loss
            previous_loss = loss.item()
        # Validation loop
        with torch.no_grad():
            for inputs, targets in val_loader:
                outputs = model(inputs)
                loss = loss_fn(outputs, targets)
                # Update the learning rate if the loss has increased
                update_lr_if_loss_increased(optimizer, previous_loss)
        print(f'Epoch {epoch+1}/{epochs}, Loss: {loss.item():.4f}')
# Train the model
train_model(optim, model, loss_fn, train_loader, val_loader, epochs=10)


optim = torch.optim.SGD(..., lr=0.005)
# Define a function to update the learning rate
def update_learning_rate(optimizer, new_lr):
    for param_group in optimizer.param_groups:
        param_group['lr'] = new_lr
# Define a function to check if the loss has increased
def check_loss_increase(loss, previous_loss):
    if loss > previous_loss:
        return True
    else:
        return False
# Define a function to update the learning rate if the loss has increased
def update_lr_if_loss_increased(optimizer, previous_loss):
    if check_loss_increase(loss, previous_loss):
        update_learning_rate(optimizer, 0.001)  # Set the new learning rate
# Define a function to train the model
def train_model(optimizer, model, loss_fn, train_loader, val_loader, epochs):
    previous_loss = float('inf')
    for epoch in range(epochs):
        # Training loop
        for inputs, targets in train_loader:
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = loss_fn(outputs, targets)
            loss.backward()
            optimizer.step()
            # Update the previous loss
            previous_loss = loss.item()
        # Validation loop
        with torch.no_grad():
            for inputs, targets in val_loader:
                outputs = model(inputs)
                loss = loss_fn(outputs, targets)
                # Update the learning rate if the loss has increased
                update_lr_if_loss_increased(optimizer, previous_loss)
        print(f'Epoch {epoch+1}/{epochs}, Loss: {loss.item():.4f}')
# Train the model
train_model(optim, model, loss_fn, train_loader, val_loader, epochs=10)


# get the word2vec embeddings
embeddings = word2vec.wv.vectors
# get the word2vec vocabulary
vocab = word2vec.wv.index_to_key
# create a dictionary to map words to their embeddings
embedding_dict = {word: embedding for word, embedding in zip(vocab, embeddings)}
# create a PyTorch embedding layer
embedding_layer = torch.nn.Embedding(len(vocab), embeddings.shape[1])
# initialize the embedding layer with the word2vec embeddings
embedding_layer.weight.data.copy_(torch.from_numpy(np.array(list(embedding_dict.values()))))
# create a tensor of indices for the input data
input_indices = torch.tensor(input_Tensor, dtype=torch.long)
# get the embeddings for the input data
embedded_input = embedding_layer(input_indices)


    # get the word2vec embedding
    word2vec_embedding = word2vec.wv
    # get the word2vec embedding matrix
    word2vec_embedding_matrix = word2vec_embedding.vectors
    # get the word2vec embedding matrix shape
    word2vec_embedding_matrix_shape = word2vec_embedding_matrix.shape
    # get the word2vec embedding matrix dtype
    word2vec_embedding_matrix_dtype = word2vec_embedding_matrix.dtype
    # get the word2vec embedding matrix device
    word2vec_embedding_matrix_device = word2vec_embedding_matrix.device
    # get the word2vec embedding matrix is_cuda
    word2vec_embedding_matrix_is_cuda = word2vec_embedding_matrix.is_cuda
    # get the word2vec embedding matrix is_pinned
    word2vec_embedding_matrix_is_pinned = word2vec_embedding_matrix.is_pinned
    # get the word2vec embedding matrix is_sparse
    word2vec_embedding_matrix_is_sparse = word2vec_embedding_matrix.is_sparse
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is_contiguous
    word2vec_embedding_matrix_is_contiguous = word2vec_embedding_matrix.is_contiguous
    # get the word2vec embedding matrix is


# convert torch tensor to numpy array
x_np = x.numpy()
# convert numpy array to pandas dataframe
px = pd.DataFrame(x_np)


# convert tensor to numpy array
x_np = x.numpy()
# convert numpy array to dataframe
px = pd.DataFrame(x_np)


C = torch.from_numpy(np.take(B, A_log, axis=1))


C = torch.LongTensor(np.where(A_logical, B, 0))


    # convert A_log to a numpy array
    A_log = A_log.numpy()
    # convert B to a numpy array
    B = B.numpy()
    # convert A_log to a boolean array
    A_log = A_log.astype(bool)
    # use numpy indexing to get the columns that correspond to a 1 value in A_log
    C = B[:, A_log]
    # convert C to a torch tensor
    C = torch.from_numpy(C)
    return C
    ### END SOLUTION


C = torch.LongTensor(np.where(A_log, B, 0))


x_array = load_data()
x_tensor = torch.tensor(x_array)


    return torch.tensor(a)
    ### END SOLUTION
x_tensor = Convert(x_array)


mask = torch.zeros((len(lens), max(lens)), dtype=torch.long)
for i, l in enumerate(lens):
    mask[i, :l] = 1


    mask = np.zeros((len(lens), max(lens)))
    for i, l in enumerate(lens):
        mask[i, :l] = 1
    mask = torch.from_numpy(mask).long()
    ### END SOLUTION
    return mask


    result = torch.zeros((t.shape[0], t.shape[1], t.shape[1]))
    for i in range(t.shape[0]):
        result[i] = torch.diag(t[i])
    return result
    ### END SOLUTION


    # a = torch.stack((a, b), 0)
    # ab = torch.stack((a, b), 0)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)
    # ab = torch.cat((a, b), -1)
    # ab = torch.cat((a, b), 0)
    # ab = torch.cat((a, b), 1)


a = torch.zeros_like(a)
for i, length in enumerate(lengths):
    a[i, length:, :] = 0


a = torch.zeros_like(a)
for i, length in enumerate(lengths):
    a[i, :length, :] = 2333


a = torch.zeros_like(a)
for i, length in enumerate(lengths):
    a[i, :length, :] = 0


    tensor_of_tensors = torch.tensor(lt)
    return tensor_of_tensors
    ### END SOLUTION


result = torch.gather(t, 1, idx.unsqueeze(1)).squeeze(1)


result = torch.gather(t, 1, idx.unsqueeze(1).expand_as(t)).squeeze(1)


result = torch.gather(x, 1, ids.unsqueeze(-1).expand(-1, -1, 2))


result = torch.gather(x, 1, ids.unsqueeze(-1).expand(-1, -1, x.size(-1)))


result = torch.gather(x, 1, ids.unsqueeze(-1).expand(-1, -1, 2)).squeeze(-1)


y = torch.argmax(softmax_output, dim=1).unsqueeze(1)


    y = torch.argmax(softmax_output, dim=1).unsqueeze(1)
    return y
    ### END SOLUTION


softmax_output = torch.tensor(softmax_output)
y = torch.argmin(softmax_output, dim=1)


def cross_entropy2d(input, target, weight=None, size_average=True):
    # input: (n, c, w, z), target: (n, w, z)
    n, c, w, z = input.size()
    # log_p: (n, c, w, z)
    log_p = F.log_softmax(input, dim=1)
    # log_p: (n*w*z, c)
    log_p = log_p.permute(0, 3, 2, 1).contiguous().view(-1, c)  # make class dimension last dimension
    log_p = log_p[
       target.view(n, w, z, 1).repeat(0, 0, 0, c) >= 0]  # this looks wrong -> Should rather be a one-hot vector
    log_p = log_p.view(-1, c)
    # target: (n*w*z,)
    mask = target >= 0
    target = target[mask]
    loss = F.nll_loss(log_p, target.view(-1), weight=weight, size_average=False)
    if size_average:
        loss /= mask.data.sum()
    return loss


cnt_not_equal = torch.sum(A != B).item()


    cnt_equal = torch.sum(A == B)
    ### END SOLUTION
    return cnt_equal


cnt_equal = torch.sum(A[-len(B):] == B).item()


tensors_31 = []
for i in range(31):
    start = i * chunk_dim
    end = start + chunk_dim
    tensors_31.append(a[:, :, :, start:end, :])


tensors_31 = []
for i in range(31):
    start = i * chunk_dim
    end = start + chunk_dim
    tensors_31.append(a[:, :, start:end, :, :])


output = torch.where(mask == 1, clean_input_spectrogram, output)


mask = mask.squeeze(0).numpy()
clean_input_spectrogram = clean_input_spectrogram.squeeze(0).numpy()
output = output.squeeze(0).numpy()
output[mask == 0] = clean_input_spectrogram[mask == 0]
output = torch.from_numpy(output)


signed_min = torch.sign(x) * torch.min(torch.abs(x), torch.abs(y)) + torch.sign(y) * torch.min(torch.abs(x), torch.abs(y))


signed_max = torch.sign(x) * torch.max(torch.abs(x), torch.abs(y)) + torch.sign(y) * torch.max(torch.abs(x), torch.abs(y))


    # x = torch.tensor(x)
    # y = torch.tensor(y)
    # x_sign = torch.sign(x)
    # y_sign = torch.sign(y)
    # x_abs = torch.abs(x)
    # y_abs = torch.abs(y)
    # min_abs = torch.min(x_abs, y_abs)
    # signed_min = x_sign * min_abs
    # return signed_min
    ### END SOLUTION
    return signed_min


conf, classes = torch.max(output.reshape(1, 3), 1)
class_names = '012'
confidence_score = torch.softmax(classes, dim=0)


# Convert tensors to numpy arrays
a_np = a.numpy()
b_np = b.numpy()

# Create a new numpy array with the desired shape
result_np = np.zeros((a_np.shape[0], 5))

# Fill the first two columns with the first two columns of 'a'
result_np[:, :2] = a_np[:, :2]

# Fill the last two columns with the last two columns of 'b'
result_np[:, 2:4] = b_np[:, 2:4]

# Calculate the average of the middle column
result_np[:, 4] = (a_np[:, 3] + b_np[:, 0]) / 2

# Convert the result back to a tensor
result = torch.from_numpy(result_np)


    # convert to numpy
    a = a.numpy()
    b = b.numpy()
    # convert to pandas
    a = pd.DataFrame(a)
    b = pd.DataFrame(b)
    # merge
    result = pd.concat([a, b], axis=1)
    # calculate the average
    result = result.mean(axis=1)
    # convert to torch
    result = torch.from_numpy(result.values)
    ### END SOLUTION
    return result


result = torch.cat([t, torch.zeros(1, 4, 2)], dim=0)


result = torch.cat([t, torch.zeros(1, 2, 2)], dim=0)


# Create a new tensor with the desired shape
new_tensor = torch.full((t.shape[0], 4, 2), -1)

# Stack the original tensor and the new tensor along the first dimension
result = torch.cat((t, new_tensor), dim=0)