plt.plot(x, y, label="y over x")
plt.legend(title="Legend")
plt.show()


plt.plot(x, y, label="y over x")
plt.legend(title="Legend", title_fontsize="16", loc="upper left", fontsize="16", fontweight="bold")


plt.hist(x, 10, color='blue', edgecolor='black', linewidth=1.2)
plt.hist(y, 10, color='red', edgecolor='black', linewidth=1.2)


fig, (ax1, ax2) = plt.subplots(2, 1, gridspec_kw={'height_ratios': [3, 1]})


plt.hist(x, bins, alpha=0.5, label='x')
plt.hist(y, bins, alpha=0.5, label='y')


plt.hist(x, bins=10, alpha=0.5, label='x')
plt.hist(y, bins=10, alpha=0.5, label='y')
plt.legend(loc='upper right')
plt.show()


x = [a, c]
y = [b, d]
plt.plot(x, y)
plt.xlim(0, 5)
plt.ylim(0, 5)


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
cax1 = ax1.matshow(x, cmap='viridis')
cax2 = ax2.matshow(y, cmap='viridis')
fig.colorbar(cax1, ax=ax1)
fig.colorbar(cax2, ax=ax2)


for i in range(x.shape[1]):
    plt.plot(x[:, i], label=f"a{i}")


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.plot(y, x, label="Y over X")
ax1.set_title("Y and Z")
ax2.plot(z, a, label="Z over A")
ax2.set_title("Y and Z")
fig.suptitle("Y and Z")


plt.plot(*zip(*points), marker='o')
plt.yscale('log')
plt.show()


plt.plot(x, y)
plt.title("y over x", fontsize=20)
plt.xlabel("x", fontsize=18)
plt.ylabel("y", fontsize=16)


ax.plot(x, y, 'o')
ax.set_xticks(x)
ax.set_xticklabels(x)


def plot_lines(lines, c):
    for i in range(len(lines)):
        x = [lines[i][0][0], lines[i][1][0]]
        y = [lines[i][0][1], lines[i][1][1]]
        plt.plot(x, y, color=c[i])


plt.loglog(x, y, 'o')
plt.xticks([1, 10, 100])
plt.yticks([1, 10, 100])
plt.show()


# Make a histogram of data and renormalize the data to sum up to 1
plt.hist(data, bins=10, density=True, alpha=0.6, color='g')

# Format the y tick labels into percentage and set y tick labels as 10%, 20%, etc.
plt.yticks(np.arange(0, 1.1, 0.1), [f'{x*100:.0f}%' for x in np.arange(0, 1.1, 0.1)])

# Set the x and y labels
plt.xlabel('Data')
plt.ylabel('Frequency')

# Set the title
plt.title('Histogram of Data')

# Show the plot
plt.show()


plt.plot(x, y, 'o', alpha=0.5, solid_capstyle='round')


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.plot(x, y, label='y')
ax1.plot(a, z, label='a')
ax1.legend(loc='upper left')
ax2.plot(x, y, label='y')
ax2.plot(a, z, label='a')
ax2.legend(loc='upper left')
fig.legend(loc='upper center', ncol=2)


# fig, (ax1, ax2) = plt.subplots(1, 2, sharey=False)
# sns.regplot(x="bill_length_mm", y="bill_depth_mm", data=df, ax=ax1)
# sns.regplot(x="flipper_length_mm", y="bill_length_mm", data=df, ax=ax2)


ax.set_xticklabels(['first', 'second', 'third', 'fourth', 'fifth', 'sixth', 'seventh', 'eighth', 'ninth', 'tenth'])


fig, ax = plt.subplots(1, 2, sharey=True)
ax[0].plot(x, y)
ax[1].plot(y, x)
fig.suptitle("Figure")


df.plot(kind="line", x="Index 1", y="Type A", label="Type A")
df.plot(kind="line", x="Index 1", y="Type B", label="Type B")
plt.xlabel("X")
plt.ylabel("Y")


plt.scatter(x, y, marker='|', hatch='///', linewidth=0)


plt.scatter(x, y, marker='v', edgecolor='k', hatch='|')


plt.scatter(x, y, marker='*', hatch='//')


plt.stem(x, y, 'b', basefmt='b-', linefmt='b-', markerfmt='bo')


plt.bar(d.keys(), d.values(), color=[c[x] for x in d.keys()])


x = [3]
y = [0]
plt.plot(x, y, label="cutoff")
plt.legend()


fig, ax = plt.subplots(subplot_kw=dict(polar=True))
ax.bar(labels, height, tick_label=labels)


plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
plt.grid(b=True, which='major', color='w', linestyle='-')


plt.pie(sizes, labels=labels, colors=colors, startangle=90, counterclock=False, wedgeprops={"linewidth": 5, "edgecolor": "black"})


plt.plot(x, y, 'o', markersize=10, markeredgecolor='k', markerfacecolor='w', alpha=0.5)


sns.distplot(df["bill_length_mm"], color="blue")
plt.axvline(x=55, color="green")


plt.bar(range(len(blue_bar)), blue_bar, color='b', label='Blue Bar')
plt.bar(range(len(orange_bar)), orange_bar, bottom=blue_bar, color='orange', label='Orange Bar')


fig, ax = plt.subplots(2, 1)
ax[0].plot(x, y, label='y')
ax[0].plot(z, a, label='z')
ax[0].legend()
ax[1].plot(x, y, label='y')
ax[1].plot(z, a, label='z')
ax[1].legend()


sns.catplot(x="bill_length_mm", col="species", col_wrap=2, kind="bar", data=df)


def draw_circle(ax, center_x, center_y, radius):
    x = np.arange(0, 1.01, 0.01)
    y = np.sqrt(radius**2 - (x - center_x)**2) + center_y
    y = np.append(y, center_y - np.sqrt(radius**2 - (x - center_x)**2))
    ax.plot(x, y, 'b-')
    ax.plot(x[::-1], y[::-1], 'b-')


plt.plot(x, y, label=r'$\phi$')
plt.title(r'$\phi$ is bold and $\phi$ is bold', fontweight='bold')


plt.plot(x, y, label="Line", markerscale=0.1)


plt.plot(x, y, label="Line", linewidth=0.3)


sns.pairplot(df, x_vars=["x"], y_vars=["y"], hue="id", legend=False)


plt.scatter(x, y, s=100, c='r', edgecolors='k', zorder=2)
plt.xlim(0, 10)
plt.ylim(0, 10)
plt.gca().set_clip_on(False)


plt.scatter(x, y, c='red', edgecolors='black')


fig, axs = plt.subplots(2, 2, figsize=(15, 15))
axs[0, 0].plot(x, y)
axs[0, 1].plot(x, y)
axs[1, 0].plot(x, y)
axs[1, 1].plot(x, y)


plt.hist(x, bins=5, range=(0, 10), width=2)


plt.errorbar(x, y, yerr=error, fmt='o', capsize=5)


plt.axvline(x=0, color='w')
plt.axhline(y=0, color='w')


ax.errorbar(box_position, box_height, yerr=box_errors, fmt="none", color=c)


fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.plot(y, x, label="Y")
ax1.set_title("Y")
ax2.plot(z, a, label="Z")
ax2.set_title("Z")
ax2.set_title("Z", y=1.1)
fig.suptitle("Two Subplots")


fig, ax = plt.subplots(4, 4, figsize=(5, 5))
for i in range(4):
    for j in range(4):
        ax[i, j].plot(y, x)
        ax[i, j].tick_params(axis='both', which='major', labelsize=6)
        ax[i, j].set_xticks([])
        ax[i, j].set_yticks([])


plt.matshow(d, fignum=0, cmap='gray')
plt.show()


# Create a matplotlib figure and axis
fig, ax = plt.subplots()

# Plot the data as a table
ax.table(
    cellText=df.values,
    colLabels=df.columns,
    bbox=[0, 0, 1, 1],
)

# Set the axis limits to the same as the figure
ax.set_xlim([0, 1])
ax.set_ylim([0, 1])

# Remove the axis
ax.axis("off")

# Show the plot
plt.show()


plt.plot(x, y)
plt.xlabel('x')
plt.ylabel('y')
plt.title('Line Chart')
plt.show()


plt.plot(x, y)
plt.xticks(x, rotation=45)
plt.show()


sns.catplot(x="time", y="pulse", hue="diet", col="time", data=df, kind="scatter")
plt.suptitle("Group: Fat", y=1.02)
plt.suptitle("Group: No Fat", y=1.02, x=0.6)


sns.catplot(x="time", y="pulse", hue="diet", col="time", data=df, kind="scatter")
plt.xlabel("Exercise Time")
plt.ylabel("Exercise Pulse")


sns.catplot(x="time", y="pulse", hue="diet", col="kind", data=df, kind="scatter", yorder=["rest", "workout"])


plt.plot(x, y, label="y")
plt.legend(fontsize=8)


plt.plot(x, y, color='red', linewidth=2, linestyle='--')
plt.show()


plt.plot(x, y, label="y")
plt.legend(frameon=False)


plt.plot(t, a, label='a')
plt.plot(t, b, label='b')
plt.plot(t, c, label='c')
plt.legend()


sns.stripplot(x="sex", y="bill_length_mm", hue="species", data=df)
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)


g = sns.FacetGrid(df, col="b", col_wrap=3)
g.map(sns.pointplot, "a", "c", ci=None)
g.set_xticks(np.arange(1, 31, 2))
g.set_xticklabels(np.arange(2, 32, 2))


fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z, c='r', marker='o')
ax.view_init(azim=100, elev=50)
plt.show()


plt.plot(x, y)
plt.xlabel("x")
plt.ylabel("y")
plt.show()


gs = gridspec.GridSpec(nrow, ncol, wspace=0, hspace=0)
for i in range(nrow):
    for j in range(ncol):
        ax = fig.add_subplot(gs[i, j], xticks=[], yticks=[])
        ax.imshow(x, cmap=plt.cm.gray_r, interpolation='nearest')


# Convert the list of labels to a one-hot encoded tensor
result = tf.one_hot(labels, depth=10, dtype=tf.int32)


# Convert the list of labels to a tensor
labels_tensor = tf.constant(labels, dtype=tf.int32)

# Create a one-hot encoded tensor for each class
num_classes = 10
one_hot_tensor = tf.one_hot(labels_tensor, depth=num_classes, dtype=tf.int32)

# Reshape the tensor to have a single dimension
result = tf.reshape(one_hot_tensor, (-1, num_classes))

# Print the result
print(result)


    # Convert the list of integers to a one-hot tensor
    num_classes = 10
    one_hot_labels = tf.one_hot(labels, depth=num_classes, dtype=tf.int32)
    return one_hot_labels
    ### END SOLUTION


def my_map_func(i):
  return [[i, i+1, i+2]]       # Fyi [[i], [i+1], [i+2]] throws an exception
ds = tf.data.Dataset.from_tensor_slices(input)
ds = ds.flat_map(lambda x: tf.data.Dataset.from_tensor_slices(my_map_func(x)))
result = []
with tf.compat.v1.Session() as sess:
  for _ in range(9):
    result.append(sess.run(ds))
print(result)


    return tf.stack([input, input+1, input+2])
    ### END SOLUTION

ds = tf.data.Dataset.from_tensor_slices(example_input)
ds = ds.flat_map(lambda x: tf.data.Dataset.from_tensor_slices(f(x)))
result = []
with tf.compat.v1.Session() as sess:
    for _ in range(9):
        result.append(sess.run(ds.make_one_shot_iterator().get_next()))
print(result)


result = tf.concat([
    tf.ones([8 - len(lengths), 1]),
    tf.zeros([len(lengths), 1]),
    tf.ones(lengths, dtype=tf.int32)
], axis=0)


result = tf.pad(tf.ones(lengths), [(0, 8 - sum(lengths))], constant_values=1)


result = tf.keras.preprocessing.sequence.pad_sequences(
    [tf.zeros(lengths[i], dtype=tf.int32) + 1 for i in range(len(lengths))],
    value=0,
    padding='pre',
    maxlen=8,
    dtype=tf.int32
)


    return tf.one_hot(lengths, 8, on_value=1, off_value=0)
    ### END SOLUTION


result = tf.concat([tf.ones([8 - len(lengths), 1]), tf.zeros([len(lengths), 1])], axis=0)


result = tf.map_fn(lambda x: tf.stack([x[0], x[1]]), tf.meshgrid(a, b), dtype=tf.int32)


    return tf.stack(tf.meshgrid(a,b, indexing='ij'), axis=-1)
    ### END SOLUTION


result = tf.expand_dims(tf.expand_dims(a, 0), 3)


result = tf.reduce_sum(tf.square(tf.subtract(a, b)), axis=1)


    # tf.square(A - B)
    # tf.reduce_sum(tf.square(A - B), axis=1)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32)
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result')
    # tf.reduce_sum(tf.square(A - B), axis=1, keepdims=True, name='result', output_type=tf.int32, dtype=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf.int32, name='result', output_type=tf


result = tf.gather_nd(x, tf.stack([y, z], axis=1))


result = tf.gather_nd(x, tf.stack([row, col], axis=1))


    result = x[y,z]
    ### END SOLUTION
    return result
result = f()
print(result)


result = tf.einsum('ijk,ilk->ijl', A, B)


result = tf.strings.unicode_decode(x, 'UTF-8')


    result = tf.strings.unicode_decode(x, 'UTF-8')
    ### END SOLUTION
    return result


result = tf.reduce_sum(x, axis=-2) / tf.cast(tf.math.count_nonzero(x, axis=-2), tf.float32)


result = tf.math.unsorted_segment_mean(x, tf.math.reduce_sum(tf.cast(x != 0, tf.int32), axis=-1), tf.shape(x)[0])


    # Get the number of non-zero entries in each feature vector
    num_non_zero = tf.reduce_sum(tf.cast(tf.not_equal(x, 0), tf.float32), axis=-1)
    # Calculate the sum of each feature vector
    sum_features = tf.reduce_sum(x, axis=-1)
    # Calculate the average of each feature vector
    avg_features = sum_features / num_non_zero
    return avg_features
    ### END SOLUTION
result = f(x)
print(result)


    return tf.argmax(a, axis=1)
    ### END SOLUTION


result = tf.random.uniform(shape=(10,), minval=1, maxval=5, dtype=tf.int32, seed=seed_x)


result = tf.random.uniform(shape=(114,), minval=2, maxval=6, dtype=tf.int32, seed=seed_x)


    tf.random.set_seed(seed_x)
    result = tf.random.uniform(shape=(10,), minval=1, maxval=5, dtype=tf.int32)
    ### END SOLUTION
    return result

f()


result = scipy.optimize.curve_fit(lambda x, A, B: A * np.log(x) + B, x, y)[0]


import numpy as np
import scipy.optimize
y = np.array([1, 7, 20, 50, 79])
x = np.array([10, 19, 30, 35, 51])
p0 = (4, 0.1, 1)
result = scipy.optimize.curve_fit(lambda p, x: p[0]*np.exp(p[1]*x) + p[2], x, y, p0)


statistic, p_value = stats.ks_2samp(x, y)


result = stats.ks_2samp(x, y).pvalue < alpha


result = optimize.minimize(lambda x: sqrt((sin(pi/2) + sin(0) + x[0] - 2)**2 + (cos(pi/2) + cos(0) + x[1] - 1)**2 + sin(x[2]) + cos(x[2]) + 4), initial_guess)


p_values = scipy.stats.norm.sf(np.abs(z_scores)) * 2


result = stats.lognorm.cdf(x, s=stddev, scale=np.exp(mu))


expected_value = np.exp(mu + stddev**2 / 2)
median = np.exp(mu)


    return sA*sB
    ### END SOLUTION
f()


# Create a grid of points to interpolate on
x, y, z = np.meshgrid(points[:, 0], points[:, 1], points[:, 2])
# Flatten the grid points and values
x_flat, y_flat, z_flat = x.flatten(), y.flatten(), z.flatten()
V_flat = V
# Create a linear interpolation function
interpolator = scipy.interpolate.LinearNDInterpolator((x_flat, y_flat, z_flat), V_flat)
# Interpolate the value at the requested point
result = interpolator(request[:, 0], request[:, 1], request[:, 2])


# Create a grid of x, y, z values
x, y, z = np.meshgrid(points[:, 0], points[:, 1], points[:, 2])

# Create a function that interpolates V based on x, y, z
f = scipy.interpolate.LinearNDInterpolator(list(zip(x.ravel(), y.ravel(), z.ravel())), V.ravel())

# Interpolate the values at the requested points
result = f(request[:, 0], request[:, 1], request[:, 2])


# Calculate the rotation matrix
theta = np.radians(angle)
rotation_matrix = np.array([[np.cos(theta), -np.sin(theta)],
                            [np.sin(theta), np.cos(theta)]])
# Calculate the translation vector
translation_vector = np.array([x0, y0])
# Calculate the rotated coordinates
xrot, yrot = np.dot(rotation_matrix, translation_vector)


    # The idea is to generate a uniform distribution of points in the range [0, T]
    # and compare it to the actual times generated by the Poisson process.
    # You can use the `stats.uniform` distribution to generate the uniform points.
    # Then, you can use the `stats.ks_2samp` function to compare the two distributions.
    # The `stats.ks_2samp` function returns the KS statistic and the p-value.
    # If the p-value is greater than a certain threshold (e.g., 0.05), you can reject the null hypothesis.
    # Otherwise, you cannot reject the null hypothesis.
    # Here's a possible implementation:
    uniform_points = np.random.uniform(0, T, len(times))
    ks_statistic, p_value = stats.ks_2samp(times, uniform_points)
    return p_value
    ### END SOLUTION
print f()


Feature = sparse.hstack([c1, c2]).tocsr()


# To concatenate two csr_matrix, we can use the hstack function from scipy.sparse
# This function stacks arrays in sequence horizontally (column wise)
# We need to convert the csr_matrix to a dense array first, concatenate them, and then convert back to csr_matrix
import numpy as np
c1_dense = c1.toarray()
c2_dense = c2.toarray()
concatenated_dense = np.hstack((c1_dense, c2_dense))
Feature = sparse.csr_matrix(concatenated_dense)


def distance(x, y):
    return np.linalg.norm(x - y, axis=1)
def objective(result):
    return distance(points1[result], points2).sum()
def constraint(result):
    return distance(points1[result], points2[result]) - 1
result = scipy.optimize.minimize(objective, np.arange(N), constraints=constraint, method='SLSQP')


# Define the objective function to minimize the total Manhattan distance
def objective_function(result):
    distances = np.zeros((N, N))
    for i in range(N):
        for j in range(N):
            distances[i, j] = abs(result[i] - result[j])
    return np.sum(distances)

# Define the constraints: each point in points2 must be used exactly once
def constraint(result):
    return np.sum(result) - N

# Initial guess for the result
initial_guess = np.arange(N)

# Solve the optimization problem
result = scipy.optimize.minimize(objective_function, initial_guess, constraints={'type': 'eq', 'fun': constraint})

# Extract the result
result = result.x.astype(int)


# Define a function to check if two cells are adjacent
def are_adjacent(cell1, cell2):
    x1, y1 = cell1
    x2, y2 = cell2
    return abs(x1 - x2) + abs(y1 - y2) == 1

# Define a function to perform DFS and count regions
def count_regions(img, threshold):
    rows, cols = img.shape
    visited = np.zeros((rows, cols), dtype=bool)
    regions = 0

    def dfs(r, c):
        if r < 0 or r >= rows or c < 0 or c >= cols or visited[r, c] or img[r, c] <= threshold:
            return
        visited[r, c] = True
        for dr, dc in [(-1, 0), (1, 0), (0, -1), (0, 1), (-1, -1), (-1, 1), (1, -1), (1, 1)]:
            dfs(r + dr, c + dc)

    for r in range(rows):
        for c in range(cols):
            if not visited[r, c] and img[r, c] > threshold:
                dfs(r, c)
                regions += 1

    return regions

# Count the number of regions
result = count_regions(img, threshold)


# Define a function to check if two cells are adjacent
def are_adjacent(cell1, cell2):
    x1, y1 = cell1
    x2, y2 = cell2
    return abs(x1 - x2) + abs(y1 - y2) == 1

# Define a function to perform DFS and count regions
def count_regions(img, threshold):
    rows, cols = img.shape
    visited = np.zeros((rows, cols), dtype=bool)
    regions = 0

    def dfs(x, y):
        if x < 0 or x >= rows or y < 0 or y >= cols or visited[x, y] or img[x, y] < threshold:
            return
        visited[x, y] = True
        for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1), (-1, -1), (-1, 1), (1, -1), (1, 1)]:
            dfs(x + dx, y + dy)

    for i in range(rows):
        for j in range(cols):
            if not visited[i, j] and img[i, j] >= threshold:
                dfs(i, j)
                regions += 1

    return regions

# Count the number of regions
result = count_regions(img, threshold)


    # Use ndimage.label to label the regions
    labeled_array, num_features = ndimage.label(img > threshold)
    # Return the number of regions
    return num_features
    ### END SOLUTION


# Find the regions of cells which value exceeds a given threshold
labels, num_features = ndimage.label(img > threshold)

# Determine the center of mass of each region
centers = ndimage.center_of_mass(img, labels, range(1, num_features + 1))

# Calculate the distance between the center of mass and the top left corner
distances = np.sqrt(centers[:, 0]**2 + centers[:, 1]**2)

# Output the distances as a list
result = list(distances)


M = lil_matrix((10, 10))
for i in range(10):
    for j in range(10):
        if i != j:
            M[i, j] = np.random.rand()
            M[j, i] = M[i, j]


    sA = sA + sA.T
    return sA
    ### END SOLUTION


# Define a function to check if a cell is surrounded by zeros
def is_surrounded_by_zeros(arr, i, j):
    directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]
    for dx, dy in directions:
        ni, nj = i + dx, j + dy
        if 0 <= ni < arr.shape[0] and 0 <= nj < arr.shape[1] and arr[ni, nj] == 1:
            return False
    return True

# Create a copy of the array to avoid modifying the original
result = square.copy()

# Iterate over each cell in the array
for i in range(result.shape[0]):
    for j in range(result.shape[1]):
        if result[i, j] == 1 and is_surrounded_by_zeros(result, i, j):
            result[i, j] = 0

# Use scipy.ndimage to remove small blobs
from scipy.ndimage import label, generate_binary_structure

# Define a connectivity of 1 (8-connectivity)
structure = generate_binary_structure(2, 1)

# Label connected components
labeled_array, num_features = label(result, structure=structure)

# Remove small blobs (blobs with size less than 2)
for label_value in range(1, num_features + 1):
    if np.sum(labeled_array == label_value) < 2:
        result[labeled_array == label_value] = 0

# Convert back to binary
result = (result > 0).astype(int)


# Define a function to check if a cell is surrounded by zeros
def is_surrounded_by_zeros(arr, i, j):
    directions = [(0, 1), (1, 0), (0, -1), (-1, 0)]
    for dx, dy in directions:
        ni, nj = i + dx, j + dy
        if 0 <= ni < arr.shape[0] and 0 <= nj < arr.shape[1] and arr[ni, nj] != 0:
            return False
    return True

# Apply the function to each cell in the array
square[square > 0] = 0 if is_surrounded_by_zeros(square, np.unravel_index(np.where(square > 0)[0][0], square.shape), np.unravel_index(np.where(square > 0)[1][0], square.shape)) else square[square > 0]


mean = col.mean()
standard_deviation = col.std()


# Convert the sparse column to a dense array
dense_col = col.toarray().flatten()

# Find the maximum and minimum values
max_value = np.max(dense_col)
min_value = np.min(dense_col)

# The solution is stored in these variables
Max, Min = max_value, min_value


# Convert the sparse column to a dense array
dense_col = col.toarray().flatten()

# Calculate the median
median = np.median(dense_col)

# Calculate the mode
from scipy import stats
mode = stats.mode(dense_col)[0][0]

# Store the results in the variables
Median = median
Mode = mode


# Calculate pairwise Euclidean distances between all regions
distances = scipy.spatial.distance.pdist(example_array, 'euclidean')
# Convert distances to a square matrix
distance_matrix = scipy.spatial.distance.squareform(distances)
# Find the minimum distance between each pair of regions
min_distances = np.min(distance_matrix, axis=1)
# Create a matrix to store the results
result = np.column_stack((np.arange(len(min_distances)), np.arange(len(min_distances)), min_distances))


# Calculate pairwise Manhattan distances between all regions
distances = scipy.spatial.distance.cdist(example_array, example_array, 'cityblock')
# Convert distances to a 2D array with indices
result = np.zeros((example_array.shape[0], example_array.shape[0]))
for i in range(example_array.shape[0]):
    for j in range(example_array.shape[0]):
        result[i, j] = distances[i, j]
# Add self distances to the diagonal
np.fill_diagonal(result, 0)
# Convert to a list of tuples (from ID, to ID, distance)
result = list(zip(np.arange(example_array.shape[0]), np.arange(example_array.shape[0]), result.flatten()))


    # Convert the array to a list of tuples
    points = [(i, j) for i in range(example_array.shape[0]) for j in range(example_array.shape[1])]
    # Calculate the pairwise distances
    distances = scipy.spatial.distance.cdist(points, points, 'euclidean')
    # Convert the distances to a 2D array
    distances = distances.reshape(example_array.shape[0], example_array.shape[1])
    # Create a list of all possible pairs of indices
    pairs = [(i, j) for i in range(example_array.shape[0]) for j in range(example_array.shape[1])]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]
    # Create a list of all possible pairs of distances
    distances_pairs = [(i, j, distances[i, j]) for i, j in pairs]


result = np.zeros((5, 100))
for i in range(5):
    tck = interpolate.splrep(x[:, i], y[:, i], k = 2, s = 4)
    result[i, :] = interpolate.splev(x_val, tck, der = 0)


statistic, critical_values, significance_level = ss.anderson_ksamp([x1, x2, x3, x4])


def kendall_tau_rolling(df, window):
    # Create a new column 'AB' with NaN values
    df['AB'] = np.nan
    # Iterate over the DataFrame with a rolling window
    for i in range(window, len(df)):
        # Extract the current window
        current_window = df.iloc[i-window:i]
        # Calculate Kendall Tau for the current window
        tau, _ = stats.kendalltau(current_window['A'], current_window['B'])
        # Assign the result to the 'AB' column for the current row
        df.at[i, 'AB'] = tau
    return df

# Apply the function to the DataFrame with a rolling window of 3
result = kendall_tau_rolling(df, 3)


p_value = stats.ranksums(pre_course_scores, during_course_scores).pvalue


    p_value = stats.ranksums(pre_course_scores, during_course_scores).pvalue
    ### END SOLUTION
    return p_value


from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skewtest
from scipy.stats import kurtosistest
from scipy.stats import kurtosis
from scipy.stats import moment
from scipy.stats import skew
from scipy.stats import skew


kurtosis_result = scipy.stats.kurtosis(a, fisher=False)


# Create a grid of (s, t) values
s_values = np.linspace(-1, 1, 50)
t_values = np.linspace(-2, 0, 50)
s_grid, t_grid = np.meshgrid(s_values, t_values)

# Interpolate the function over the grid
interpolated_function = scipy.interpolate.interp2d(s_values, t_values, z, kind='cubic')

# Evaluate the interpolated function at the (s, t) grid points
result = interpolated_function(s_grid, t_grid)


    return scipy.interpolate.interp2d(s, t, z, kind='cubic')
    ### END SOLUTION


import scipy.spatial
points = [[0,0], [1,4], [2,3], [4,1], [1,1], [2,2], [5,3]]
vor = scipy.spatial.Voronoi(points)
extraPoints = [[0.5,0.2], [3, 0], [4,0],[5,0], [4,3]]
result = scipy.spatial.Voronoi.find_simplex(extraPoints)


# Create a list of indices for each vector
indices = [np.arange(len(v)) for v in vectors]

# Create a list of lengths for each vector
lengths = [len(v) for v in vectors]

# Find the maximum length
max_length = max(lengths)

# Create a list of padded vectors
padded_vectors = [np.pad(v, (0, max_length - len(v)), 'constant') for v in vectors]

# Create a sparse matrix from the padded vectors
result = sparse.csr_matrix((np.ones(len(padded_vectors)), (np.concatenate(indices), np.concatenate([np.arange(len(v)) for v in padded_vectors]))), shape=(len(vectors), max_length))


# Define the 3x3 kernel with a shift of one cell to the right
kernel = np.ones((3, 3)) / 9
kernel = np.roll(kernel, 1, axis=1)

# Perform median filtering
b = scipy.ndimage.median_filter(a, size=3, footprint=kernel)


result = [M[row[i], column[i]] for i in range(len(row))]


new_array = scipy.interpolate.interpn((x, x, x), array, (x_new, x_new, x_new), method='linear')


    dev = abs((x-u)/o2)
    P_inner = scipy.integrate(NDfx,-dev,dev)
    P_outer = 1 - P_inner
    P = P_inner + P_outer/2
    return(P)
    ### END SOLUTION


# Create an N x N identity matrix
I = np.eye(N)
# Compute the DCT of the identity matrix
DCT_I = sf.dctn(I, norm='ortho')
# Normalize the DCT matrix
DCT_I = DCT_I / np.sqrt(N)
# Return the DCT matrix
result = DCT_I


result = sparse.diags([matrix[0], matrix[1], matrix[2]], [-1, 0, 1], shape=(5, 5), format='csr')


result = np.array([[scipy.stats.binom.pmf(j, i, p) for j in range(i+1)] for i in range(N+1)])


# Calculate the mean and standard deviation for each column
means = df.mean()
stds = df.std()

# Calculate the z-scores
z_scores = (df - means) / stds

# Convert the z-scores to a string format
z_scores_str = z_scores.applymap(lambda x: f"{x:.4f}")

# Create the result DataFrame
result = pd.DataFrame(z_scores_str, index=df.index, columns=df.columns)


# Calculate the z-scores for each column
z_scores = df.apply(stats.zscore)

# Assign the z-scores to the original DataFrame
df = z_scores

# Display the result
result = df


# Calculate z-scores for each column
z_scores = df.apply(stats.zscore)

# Create a new DataFrame to hold the results
result = pd.concat([df, z_scores], axis=1)

# Rename the columns to include 'zscore'
result.columns = ['data'] + [f'{col}_zscore' for col in result.columns[1:]]

# Display the result
result


# Calculate z-scores for each column
z_scores = df.apply(lambda x: stats.zscore(x), axis=0)

# Round the z-scores to 3 decimal places
z_scores = z_scores.round(3)

# Create a new DataFrame with the original data and z-scores
result = pd.concat([df, z_scores], axis=1)

# Rename the columns
result.columns = ['data', 'sample1', 'sample2', 'sample3', 'zscore']

# Display the result
print(result)


result = scipy.optimize.line_search(test_func, test_grad, starting_point, direction)


# Create a 2D array of coordinates
coords = np.mgrid[0:shape[0], 0:shape[1]].reshape(2, -1).T
# Calculate the Euclidean distance from the center
center = np.array([shape[0] // 2, shape[1] // 2])
distances = distance.cdist(coords, center, 'euclidean')
# Reshape the distances to match the desired output shape
result = distances.reshape(shape)


# Calculate the center of the image
center = np.array([shape[0] // 2, shape[1] // 2])

# Create a meshgrid of coordinates
y, x = np.indices(shape)

# Calculate the Manhattan distance from the center to each point
manhattan_distances = np.abs(y - center[0]) + np.abs(x - center[1])

# Create the 2D array with the same shape as the image
result = manhattan_distances


    mid = np.array([[3, 3]]).repeat(shape, axis = 0)
    result = distance.cdist(np.indices(shape).reshape(2, -1).T, mid, 'euclidean')
    ### END SOLUTION
    return result


result = scipy.ndimage.zoom(x, (shape[0] / x.shape[0], shape[1] / x.shape[1]), order=1)


def func(x, a):
    return np.dot(a, x**2)

def residual(pars, a, y):
    x = pars['x']
    model = func(x, a)
    return (y - model) ** 2

def main():
    # simple one: a(M,N) = a(3,5)
    a = np.array([[0, 0, 1, 1, 1],
                  [1, 0, 1, 0, 1],
                  [0, 1, 0, 1, 0]])
    # true values of x
    x_true = np.array([10, 13, 5, 8, 40])
    # data without noise
    y = func(x_true, a)
    #************************************
    # Apriori x0
    x0 = np.array([2, 3, 1, 4, 20])
    fit_params = Parameters()
    fit_params.add('x', value=x0)
    out = minimize(residual, fit_params, args=(a, y))
    print(out)

if __name__ == '__main__':
    main()


import scipy.optimize
import numpy as np
np.random.seed(42)
a = np.random.rand(3,5)
x_true = np.array([10, 13, 5, 8, 40])
y = a.dot(x_true ** 2)
x0 = np.array([2, 3, 1, 4, 20])
x_lower_bounds = x_true / 2
def residual(x, a, y):
    model = a.dot(x ** 2)
    return (y - model) ** 2
def objective(x, a, y):
    return np.sum(residual(x, a, y))
result = scipy.optimize.minimize(objective, x0, args=(a, y), bounds=[(lb, None) for lb in x_lower_bounds])
x_optimal = result.x
print(x_optimal)


def dN1_dt(t, N1):
    return -100 * N1 + np.sin(t)
sol = solve_ivp(fun=dN1_dt, t_span=time_span, y0=[N0,])
result = sol.y


def dN1_dt(t, N1):
    return -100 * N1 + t - np.sin(t) if 0 < t < 2 * np.pi else 2 * np.pi
sol = scipy.integrate.solve_ivp(fun=dN1_dt, t_span=time_span, y0=[N0,])
result = sol.y


def dN1_dt(t, N1):
    return -100 * N1 - np.cos(t)
sol = solve_ivp(fun=dN1_dt, t_span=time_span, y0=[N0,])
result = sol.y


for t in range (4):
    def const(x):
        y=x[t]
        return y
    cons.append({'type':'ineq', 'fun': const})


import scipy.integrate
c = 5
low = 0
high = 1
I = []
for n in range(len(c)):
    eqn = 2*x*c[n]
    result, error = scipy.integrate.quad(lambda x: eqn, low, high)
    I.append(result)
I = array(I)


    return scipy.integrate.quad(lambda x: 2*c*x, low, high)[0]
    ### END SOLUTION
I = []
for c in [1, 2, 3, 4, 5]:
    I.append(f(c))
I = array(I)


V = sparse.coo_matrix((V.data + x * V.nnz, (V.row, V.col)), shape = V.shape)


V = sparse.coo_matrix((V.data + x, (V.row, V.col)), shape = V.shape)


sa = sparse.random(10, 10, density = 0.3, format = 'csc', random_state = 42)


#csr sparse matrix
sa = sparse.random(10, 10, density = 0.3, format = 'csr', random_state = 42)
#iterate through columns
for Col in xrange(sa.shape[1]):
   Column = sa[:,Col].data
   List = [x**2 for x in Column]
   #get the column length
   Len = math.sqrt(sum(List))
   #here I assumed dot(number,Column) would do a basic scalar product
   sa[:,Col] = (1/Len)*Column


# Convert the matrix to a binary matrix
binary_matrix = (a > 0).astype(int)


# Convert the integer matrix to a binary matrix
binary_matrix = (a > 0).astype(int)


# Calculate the centroid of each cluster
cluster_centroids = scipy.cluster.hierarchy.fclusterdata(data, t=1, criterion='distance')
# Calculate the distance from each point to its cluster centroid
distances = scipy.spatial.distance.cdist(data, centroids[cluster_centroids - 1], 'euclidean')
# Find the index of the closest centroid for each point
closest_centroid_indices = np.argmin(distances, axis=1)
result = closest_centroid_indices


# Calculate the centroid of each cluster
cluster_centroids = np.array([np.mean(data[labels == i], axis=0) for i in range(len(np.unique(labels)))])
# Calculate the distance from each data point to each centroid
distances = scipy.spatial.distance.cdist(data, cluster_centroids)
# Find the closest centroid for each data point
closest_centroids = np.argmin(distances, axis=1)
# Extract the closest data point for each cluster
result = data[closest_centroids]


# Calculate the distance matrix between the centroids and the data points
distances = scipy.spatial.distance.cdist(centroids, data, 'euclidean')
# Calculate the distance to the k-th closest centroid for each data point
kth_closest_distances = np.partition(distances, k, axis=1)[:, k-1]
# Find the indices of the k-th closest centroids
kth_closest_centroids = np.argmin(distances, axis=1)
# Find the indices of the k-th closest data points for each centroid
kth_closest_data_points = np.argmin(distances[kth_closest_centroids, :], axis=1)
# Create the result array
result = kth_closest_data_points


result = fsolve(lambda a: eqn(xdata, a, bdata), x0=np.zeros(len(xdata)))


result = []
for x in xdata:
    # Use fsolve to find the root for each (x, a) pair
    b_solution = fsolve(lambda b: eqn(x, adata, b), x0=0.5)
    # Append the smaller root to the result list
    result.append(b_solution[np.argmin(b_solution)])


# Define the sample data
sample_data = [1.5,1.6,1.8,2.1,2.2,3.3,4,6,8,9]

# Define the estimated parameters
estimated_a, estimated_m, estimated_d = 1,1,1

# Define the function to be tested
def bekkers(x, a, m, d):
    p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
    return(p)

# Define the range for the KSTest
range_start = 1
range_end = 10

# Perform the KSTest
result = stats.kstest(sample_data, bekkers, args=(estimated_a, estimated_m, estimated_d), alternative='two-sided')

# Print the result
print(result)


# Define the sample data
sample_data = [1.5,1.6,1.8,2.1,2.2,3.3,4,6,8,9]

# Define the parameters of the fitted function
estimated_a, estimated_m, estimated_d = 1,1,1

# Define the function to be tested
def bekkers(x, a, m, d):
    p = a*np.exp((-1*(x**(1/3) - m)**2)/(2*d**2))*x**(-2/3)
    return(p)

# Define the range for the KStest
range_start = 1
range_end = 10

# Perform the KStest
result = stats.kstest(sample_data, bekkers, args=(estimated_a, estimated_m, estimated_d))

# Check if the null hypothesis can be rejected at 95% confidence level
reject = result.pvalue < 0.05

# Print the result
print(reject)


df['Time'] = pd.to_datetime(df['Time'])
integral_df = df.groupby(pd.Grouper(freq='25S')).apply(lambda x: integrate.trapz(x['A'], x.index))


from scipy.interpolate import griddata
x = [(2,2), (1,2), (2,3), (3,2), (2,1)]
y = [5,7,8,10,3]
eval = [(2.7, 2.3)]
result = griddata(x, y, eval, method='linear')


def log_likelihood(weights):
    # calculate the log-likelihood of the data given the weights
    # weights is a 1D array of probabilities
    # a is a 1D array of data
    # return the negative log-likelihood
    return -np.sum(np.log(weights) * a)
# use scipy.optimize.minimize to find the weights that maximize the log-likelihood
result = sciopt.minimize(log_likelihood, np.ones(len(a)) / len(a), method='BFGS')
weights = result.x


popt = sciopt.fminbound(e, pmin, pmax, args=(x,y))


result = np.where(np.abs(signal.convolve(arr, np.ones(n*2+1), mode='same') - arr) <= n)[0]


result = []
for i in range(arr.shape[0]):
    for j in range(arr.shape[1]):
        if (i == 0 or i == arr.shape[0] - 1 or j == 0 or j == arr.shape[1] - 1):
            continue
        if (arr[i, j] <= arr[i - n:i + n + 1, j - n:j + n + 1].min()):
            result.append([i, j])
result.sort()


df = df[(np.abs(stats.zscore(df)) < 3).all(axis=1)]


data1 = pd.DataFrame(data.data, columns=data.feature_names)


    # Convert the Bunch object to a DataFrame
    df = pd.DataFrame(data.data, columns=data.feature_names)
    # Add the target column to the DataFrame
    df['target'] = data.target
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target_names'] = data.target_names[data.target]
    # Add the target names to the DataFrame
    df['target


from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
df_out = pd.DataFrame(mlb.fit_transform(df['Col3']), columns=mlb.classes_, index=df.index)


df_out = pd.get_dummies(df, columns=['Col2'], prefix='Col2')


from sklearn.preprocessing import MultiLabelBinarizer
mlb = MultiLabelBinarizer()
df_out = df.copy()
df_out['Col4'] = df_out['Col4'].apply(lambda x: x if isinstance(x, list) else [])
df_out = pd.concat([df_out.drop('Col4', axis=1), pd.DataFrame(mlb.fit_transform(df_out['Col4']), columns=mlb.classes_, index=df_out.index)], axis=1)


# create a list of unique values in the last column
unique_values = df['Col3'].apply(lambda x: set(x)).unique()

# create a list of column names for the one-hot encoded columns
col_names = ['Col3_' + str(val) for val in unique_values]

# create a new dataframe with the one-hot encoded columns
df_out = pd.DataFrame(index=df.index, columns=col_names)

# loop over each row in the dataframe
for i, row in df.iterrows():
    # loop over each unique value in the last column
    for val in unique_values:
        # check if the value exists in the row
        if val in row['Col3']:
            # set the corresponding column to 1
            df_out.loc[i, 'Col3_' + str(val)] = 1
        else:
            # set the corresponding column to 0
            df_out.loc[i, 'Col3_' + str(val)] = 0


df_out = pd.get_dummies(df, columns=[df.columns[-1]], prefix=df.columns[-1])


proba = svmmodel.decision_function(x_test)
proba = 1 / (1 + np.exp(-proba))


model.fit(X, y)
proba = model.predict_proba(x_predict)


df = pd.concat([df_origin, pd.DataFrame(transform_output.toarray(), columns=transform_output.get_feature_names())], axis=1)


df = pd.DataFrame(transform_output.todense(), columns=df_origin.columns)


    # convert the sparse matrix to a dense matrix
    dense_matrix = transform_output.toarray()
    # create a new dataframe from the dense matrix
    df_transformed = pd.DataFrame(dense_matrix, columns=df.columns)
    # concatenate the original dataframe with the transformed dataframe
    result = pd.concat([df, df_transformed], axis=1)
    ### END SOLUTION
    return result


clf.steps[1] = ('pOly', PolynomialFeatures(degree=2))


steps = clf.named_steps()
steps.insert(1, ('t1919810', PCA()))
clf = Pipeline(steps)


gridsearch = GridSearchCV(model, paramGrid, verbose=verbose, cv=TimeSeriesSplit(n_splits=cv).get_n_splits([trainX, trainY]), n_jobs=n_jobs, iid=iid, fit_params={"early_stopping_rounds":42, "eval_metric" : "mae", "eval_set" : [[testX, testY]]}).fit(trainX,trainY)


gridsearch = GridSearchCV(model, paramGrid, verbose=1, cv=TimeSeriesSplit(n_splits=3).get_n_splits([trainX, trainY]), n_jobs=n_jobs, iid=iid).fit(trainX, trainY)
gridsearch.best_estimator_.fit(trainX, trainY, early_stopping_rounds=42, eval_metric="mae", eval_set=[[testX, testY]])


proba = np.zeros((len(y), 2))
for train_index, test_index in cv:
    logreg.fit(X[train_index], y[train_index])
    proba[test_index] = logreg.predict_proba(X[test_index])


proba = np.zeros((len(y), 2))
for train_index, test_index in cv:
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    logreg.fit(X_train, y_train)
    proba[test_index] = logreg.predict_proba(X_test)


import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
data = load_data()
scaler = StandardScaler()
scaler.fit(data)
scaled = scaler.transform(data)
inversed = scaler.inverse_transform(scaled)


    # YOUR CODE HERE
    # raise NotImplementedError()
    ### END SOLUTION
    return inversed


clf = GridSearchCV(bc, param_grid, cv=5, scoring='accuracy')
proba = clf.predict_proba(X_test)


def prePro(text):
    return text.lower()
tfidf = TfidfVectorizer(preprocessor=prePro)


# convert to numpy array
data = data.values
# scale
data = preprocessing.scale(data)
# convert back to dataframe
df_out = pd.DataFrame(data, columns=data.columns)


# create a copy of the original dataframe
df_out = data.copy()

# apply preprocessing.scale to the dataframe
df_out = preprocessing.scale(df_out)

# convert the dataframe to a numpy array
df_out = df_out.values

# convert the numpy array back to a dataframe
df_out = pd.DataFrame(df_out, columns=data.columns)


column_names = X.columns[clf.feature_importances_ > 0.05]


import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np
X, y = load_data()
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
column_names = X.columns


# Fit the model
km.fit(X)

# Get the cluster labels
labels = km.labels_

# Get the cluster centers
centers = km.cluster_centers_

# Get the indices of the closest 50 samples to the center of cluster p
closest_50_samples = np.argsort(np.linalg.norm(X - centers[p], axis=1))[:50]


# Solution
km.fit(X)
closest_50_samples = km.cluster_centers_[p]


# Fit the model
km.fit(X)

# Get the cluster centers
centers = km.cluster_centers_

# Get the indices of the closest 100 samples to the center p
closest_100_samples = np.argsort(np.linalg.norm(X - centers[p], axis=1))[:100]


    km.fit(X)
    centers = km.cluster_centers_
    distances = np.linalg.norm(X - centers[p], axis=1)
    samples = X[np.argsort(distances)[:50]]
    ### END SOLUTION
    return samples


# convert categorical variable to matrix
X_train = pd.get_dummies(X_train[0])

# merge back with original training data
X_train = pd.concat([X_train, X_train], axis=1)

# convert to numpy array
X_train = X_train.values

# fit the model
clf = GradientBoostingClassifier(learning_rate=0.01,max_depth=8,n_estimators=50).fit(X_train, y_train)


from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_diabetes
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
svr = SVR(kernel='rbf', C=1.0, epsilon=0.1)
svr.fit(X_train, y_train)
y_pred = svr.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")


import numpy as np
import pandas as pd
import sklearn
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
# fit, then predict X
from sklearn.svm import SVC
clf = SVC(kernel='rbf')
clf.fit(X, y)
predict = clf.predict(X)


from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.datasets import load_diabetes
X, y = load_diabetes(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
svr = SVR(kernel='poly', degree=2)
svr.fit(X_train, y_train)
y_pred = svr.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")


from sklearn.svm import SVR
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.kernel_approximation import Nystroem
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
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from sklearn.preprocessing import StandardScaler
from sklearn


import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = load_data()
assert type(queries) == list
assert type(documents) == list
tfidf = TfidfVectorizer()
tfidf.fit_transform(documents)
cosine_similarities_of_queries = tfidf.transform(queries).dot(tfidf.idf_[:, np.newaxis]).T


    cosine_similarities_of_queries = []
    for query in queries:
        query_tfidf = tfidf.transform([query])
        cosine_similarities_of_queries.append(query_tfidf.dot(tfidf.idf_).toarray())
    ### END SOLUTION
    return cosine_similarities_of_queries


# Convert the list of lists to a pandas DataFrame
df = pd.DataFrame(features, columns=['f1', 'f2', 'f3', 'f4', 'f5', 'f6'])

# One-hot encode the DataFrame
one_hot_df = pd.get_dummies(df)

# Convert the one-hot encoded DataFrame to a numpy array
new_features = one_hot_df.values


# Convert the list of lists to a DataFrame
df = pd.DataFrame(f, columns=['t1', 't2', 't3', 't4', 't5', 't6', 't7'])

# One-hot encode the DataFrame
one_hot_df = pd.get_dummies(df)

# Convert the one-hot encoded DataFrame to a numpy array
new_f = one_hot_df.values


    # convert to dataframe
    df = pd.DataFrame(features)
    # one hot encode
    df = pd.get_dummies(df)
    # return the solution in this function
    return df.values
    ### END SOLUTION


# Convert the list of lists to a pandas DataFrame
df = pd.DataFrame(features, columns=['feature'])

# One-hot encode the features
one_hot_df = pd.get_dummies(df['feature'])

# Convert the one-hot encoded DataFrame to a numpy array
new_features = one_hot_df.to_numpy()


# convert to pandas dataframe
df = pd.DataFrame(data_matrix, columns=['prof1', 'prof2', 'prof3'])
# convert to numpy array
data = df.values
# perform agglomerative clustering
model = sklearn.cluster.AgglomerativeClustering(n_clusters=2)
model.fit(data)
# get cluster labels
cluster_labels = model.labels_


# convert data_matrix to a pandas dataframe
df = pd.DataFrame(data_matrix, columns=['prof1', 'prof2', 'prof3'])

# convert dataframe to a distance matrix
dist_matrix = df.values

# perform hierarchical clustering
clustering = sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity='precomputed', linkage='complete')
cluster_labels = clustering.fit_predict(dist_matrix)

# convert cluster labels to a list
cluster_labels = list(cluster_labels)

# print the cluster labels
print(cluster_labels)


# convert to pandas dataframe
df = pd.DataFrame(simM, columns=['fruit1', 'fruit2', 'fruit3'])
# convert to distance matrix
distM = 1 - df.values
# perform agglomerative clustering
cluster = sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity='precomputed', linkage='complete')
cluster.fit(distM)
# get cluster labels
cluster_labels = cluster.labels_


from scipy.cluster.hierarchy import linkage, fcluster
from scipy.spatial.distance import squareform

# Convert the data matrix to a condensed distance matrix
dist_matrix = squareform(data_matrix)

# Perform hierarchical clustering
Z = linkage(dist_matrix, method='ward')

# Assign cluster labels
cluster_labels = fcluster(Z, t=2, criterion='maxclust')


import numpy as np
import pandas as pd
import scipy.cluster
data_matrix = load_data()
# convert to a pandas dataframe
df = pd.DataFrame(data_matrix)
# convert to a scipy sparse matrix
sparse_matrix = scipy.sparse.csr_matrix(df.values)
# perform hierarchical clustering
Z = scipy.cluster.hierarchy.linkage(sparse_matrix, method='ward')
# get the cluster labels
cluster_labels = scipy.cluster.hierarchy.fcluster(Z, t=2, criterion='maxclust')


import numpy as np
import pandas as pd
import scipy.cluster
simM = load_data()
# convert to a pandas dataframe
df = pd.DataFrame(simM, columns=['fruit1', 'fruit2', 'fruit3'])
# convert to a distance matrix
distM = 1 - df.values
# perform hierarchical clustering
Z = scipy.cluster.hierarchy.linkage(distM, method='average')
# get the cluster labels
cluster_labels = scipy.cluster.hierarchy.fcluster(Z, t=2, criterion='maxclust')


from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import


from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(data)
centered_scaled_data = scaler.transform(data)


from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Normalizer
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from sklearn.preprocessing import Normal


from sklearn.preprocessing import PowerTransformer
pt = PowerTransformer(method='box-cox')
pt.fit(data)
box_cox_data = pt.transform(data)


from sklearn.preprocessing import PowerTransformer
from sklearn.preprocessing import QuantileTransformer
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import FunctionTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import Binarizer
from sklearn.preprocessing import PolynomialFeatures
from sklearn.preprocessing import KBinsDiscretizer
from sklearn.preprocessing import OneHot


from sklearn.preprocessing import PowerTransformer
from sklearn.utils.validation import check_is_fitted

# Instantiate the transformer
pt = PowerTransformer(method='yeo-johnson', standardize=True)

# Fit the transformer to the data
pt.fit(data)

# Transform the data
yeo_johnson_data = pt.transform(data)


from sklearn.feature_extraction.text import CountVectorizer
vectorizer = CountVectorizer()
transformed_text = vectorizer.fit_transform(text)


from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
# load the iris dataset
dataset = load_iris()
# split the dataset into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.2, random_state=42)


from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(data.drop('target', axis=1), data['target'], test_size=0.2, random_state=42)


    x = data[:, :-1]
    y = data[:, -1]
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
    return x_train, y_train, x_test, y_test
    ### END SOLUTION


from sklearn.cluster import KMeans
df = load_data()
labels = KMeans(n_clusters=2, n_init=10).fit_predict(df['mse'].values.reshape(-1, 1))


from sklearn.cluster import KMeans
df = load_data()
f1 = df['mse'].values
f2 = list(range(0, len(f1)))
X = np.array(list(zip(f1, f2)))
kmeans = KMeans(n_clusters=2, n_init=10).fit(X)
labels = kmeans.predict(X)
centroids = kmeans.cluster_centers_


from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectFromModel
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import Normalizer
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.feature_selection import SelectPercentile, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import SelectFwe, f_classif
from sklearn.feature_selection import SelectFpr, f_classif
from sklearn.feature_selection import SelectFdr, f_classif
from sklearn.feature_selection import


from sklearn.feature_selection import SelectFromModel
from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.feature_selection import f_classif
from sklearn.feature_selection import SelectPercentile
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection import SelectFpr
from sklearn.feature_selection import SelectFdr
from sklearn.feature_selection import SelectFwe
from sklearn.feature_selection


    # Create a LinearSVC model with L1 penalty
    model = LinearSVC(penalty='l1', dual=False, max_iter=10000)

    # Fit the model to the data
    model.fit(X, y)

    # Get the support mask of the model
    support_mask = model.coef_.ravel() != 0

    # Get the indices of the selected features
    selected_feature_indices = np.where(support_mask)[0]

    # Get the names of the selected features
    selected_feature_names = vectorizer.get_feature_names_out()[selected_feature_indices]

    return selected_feature_names
    ### END SOLUTION


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()