Respan Dataset Explorer

import sys
import os
import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib

matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.python.keras.layers import Dense

# Redirect stdout to capture print outputs
class Tee(object):
    def __init__(self, *files):
        self.files = files

    def write(self, obj):
        for f in self.files:
            f.write(obj)
            f.flush()

    def flush(self):
        for f in self.files:
            f.flush()

output_file = open('output.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)

def save_plot(fig, filename):
    fig.savefig(filename)
    plt.close(fig)

def plot_history(history):
    # Plot training and validation accuracy
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['accuracy'])
    ax.plot(history.history['val_accuracy'])
    ax.set_title('Model Accuracy')
    ax.set_ylabel('Accuracy')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'accuracy_plot.png')

    # Plot training and validation loss
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['loss'])
    ax.plot(history.history['val_loss'])
    ax.set_title('Model Loss')
    ax.set_ylabel('Loss')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'loss_plot.png')

# Define the quantum device with 11 qubits
dev = qml.device("default.qubit", wires=11)

@qml.qnode(dev, interface='tf', diff_method='backprop')
def quantum_neural_network(inputs, encoding_weights, rot_weights):
    control_qubits = [0, 1]
    num_layers = encoding_weights.shape[0]

    # Define quantum qubit indices
    data_qubits_per_layer = []
    swap_control_qubits = []
    qubit_idx = 2  # Start from the 3rd qubit

    for layer in range(num_layers):
        data_qubits = [qubit_idx, qubit_idx + 1]  # 2 qubits per layer
        qubit_idx += 2
        data_qubits_per_layer.append(data_qubits)
        swap_control_qubit = qubit_idx
        swap_control_qubits.append(swap_control_qubit)
        qubit_idx += 1  # Add 1 for the swap control qubit

    # Process each layer
    for layer_idx, data_qubits in enumerate(data_qubits_per_layer):
        swap_control_qubit = swap_control_qubits[layer_idx]

        # Encode each feature
        for cv_idx, cv in enumerate([[0, 0], [0, 1], [1, 0], [1, 1]]):
            feature_value = inputs[cv_idx]
            angle_weight = encoding_weights[layer_idx, cv_idx]

            # Flip control qubits
            for qubit, val in zip(control_qubits, cv):
                if val == 0:
                    qml.PauliX(wires=qubit)

            # Apply controlled RY gates with feature values and weights
            for data_qubit in data_qubits:
                qml.ctrl(qml.RY, control=control_qubits)(feature_value, wires=data_qubit)
                qml.ctrl(qml.RY, control=control_qubits)(angle_weight, wires=data_qubit)

            # Restore control qubits
            for qubit, val in zip(control_qubits, cv):
                if val == 0:
                    qml.PauliX(wires=qubit)

        # Apply swap gate
        qml.Hadamard(wires=swap_control_qubit)
        qml.CSWAP(wires=[swap_control_qubit, data_qubits[0], data_qubits[1]])
        qml.Hadamard(wires=swap_control_qubit)

    # Apply rotation layers
    total_qubits = qubit_idx
    for layer in range(rot_weights.shape[0]):
        for qubit in range(total_qubits):
            rot_angles = rot_weights[layer, qubit, :]
            qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)

    # Measure swap control qubits
    results = []
    for swap_control_qubit in swap_control_qubits:
        results.append(qml.expval(qml.PauliZ(wires=swap_control_qubit)))

    return results

# Define a quantum neural network layer
class QuantumNeuralNetworkLayer(tf.keras.layers.Layer):
    def __init__(self, n_layers, n_features=4, **kwargs):
        super(QuantumNeuralNetworkLayer, self).__init__(**kwargs)
        self.n_layers = n_layers
        self.n_features = n_features

        # Define encoding weights
        self.encoding_weights = self.add_weight(
            shape=(n_layers, n_features),
            initializer=tf.keras.initializers.GlorotUniform(),
            trainable=True,
            name='encoding_weights',
            dtype=tf.float64
        )

        # Calculate total qubits = 2 control qubits + n_layers * (2 data qubits + 1 swap control qubit)
        total_qubits = 2 + n_layers * (2 + 1)

        self.rot_weights = self.add_weight(
            shape=(n_layers, total_qubits, 3),
            initializer=tf.keras.initializers.GlorotUniform(),
            trainable=True,
            name='rot_weights',
            dtype=tf.float64
        )

    def call(self, inputs):
        def apply_qnode(x):
            x = tf.cast(x, dtype=tf.float64)
            result = quantum_neural_network(x, self.encoding_weights, self.rot_weights)
            return tf.cast(result, dtype=tf.float32)

        # Reshape inputs to be compatible with tf.map_fn
        inputs = tf.reshape(inputs, (-1, self.n_features))
        outputs = tf.map_fn(
            apply_qnode,
            inputs,
            fn_output_signature=tf.float32
        )
        return tf.reshape(outputs, (-1, self.n_layers))

# Define a deep quantum neural network model
class DeepQuantumNeuralNetwork(tf.keras.Model):
    def __init__(self, n_quantum_layers, n_layers_per_qnn, num_classes, n_features=4, **kwargs):
        super(DeepQuantumNeuralNetwork, self).__init__(**kwargs)
        self.n_quantum_layers = n_quantum_layers

        # Stack multiple quantum layers
        self.quantum_layers = [
            QuantumNeuralNetworkLayer(n_layers=n_layers_per_qnn, n_features=n_features)
            for _ in range(n_quantum_layers)
        ]

        # Final classical dense layer
        self.dense = Dense(num_classes, activation='softmax')

    def call(self, inputs):
        # Pass through each quantum layer
        x = inputs
        for layer in self.quantum_layers:
            x = layer(x)
        # Apply the final dense layer
        return self.dense(x)

# Create an instance of the deep quantum neural network
n_quantum_layers = 3  # Number of quantum layers to stack
n_layers_per_qnn = 2  # Number of rotation layers per quantum neural network

model = DeepQuantumNeuralNetwork(
    n_quantum_layers=n_quantum_layers,
    n_layers_per_qnn=n_layers_per_qnn,
    num_classes=3
)

# Compile and train the model
learning_rate = 0.1
epochs = 100
batch_size = 8

model.compile(
    optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Load and preprocess data
iris = load_iris()
X = iris['data'].astype(np.float32)
y = iris['target'].reshape(-1, 1)

# One-hot encode the labels
encoder = OneHotEncoder(sparse_output=False)
y = encoder.fit_transform(y).astype(np.float32)

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=116
)

# Train the model
history = model.fit(
    x_train, y_train,
    batch_size=batch_size,
    epochs=epochs,
    verbose=1,
    validation_data=(x_test, y_test)
)

# Evaluate the model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

plot_history(history)

# Close the output file and restore stdout
sys.stdout = original_stdout
output_file.close()

print("脚本执行完成。查看 'output.log' 获取打印输出，查看当前目录获取保存的图表。")
代码出现报错Traceback (most recent call last):
  File "G:\Hellyc\yuanweihua-NewQNN-default-layers=4_4feature _AngCode\examples\yuanweihua_renew_usesoftmax_NQNN_default_4feature_AngCode_Layers=5.py", line 223, in <module>
    history = model.fit(
  File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "G:\anaconda3\envs\pennylane2\lib\site-packages\tensorflow\python\eager\execute.py", line 54, in quick_execute
    tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:

Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits' defined at (most recent call last):
    File "G:\Hellyc\yuanweihua-NewQNN-default-layers=4_4feature _AngCode\examples\yuanweihua_renew_usesoftmax_NQNN_default_4feature_AngCode_Layers=5.py", line 223, in <module>
      history = model.fit(
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
      return fn(*args, **kwargs)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1564, in fit
      tmp_logs = self.train_function(iterator)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1160, in train_function
      return step_function(self, iterator)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1146, in step_function
      outputs = model.distribute_strategy.run(run_step, args=(data,))
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1135, in run_step
      outputs = model.train_step(data)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 994, in train_step
      loss = self.compute_loss(x, y, y_pred, sample_weight)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\training.py", line 1052, in compute_loss
      return self.compiled_loss(
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__
      loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\losses.py", line 152, in __call__
      losses = call_fn(y_true, y_pred)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\losses.py", line 272, in call
      return ag_fn(y_true, y_pred, **self._fn_kwargs)
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\losses.py", line 1990, in categorical_crossentropy
      return backend.categorical_crossentropy(
    File "G:\anaconda3\envs\pennylane2\lib\site-packages\keras\backend.py", line 5535, in categorical_crossentropy
      return tf.nn.softmax_cross_entropy_with_logits(
Node: 'categorical_crossentropy/softmax_cross_entropy_with_logits'
logits and labels must be broadcastable: logits_size=[2,3] labels_size=[8,3]
	 [[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_train_function_251364]
如何进行解决
给出修改后的完整代码

The error you’re encountering:

```
InvalidArgumentError: Graph execution error:

Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits':
logits and labels must be broadcastable: logits_size=[2,3] labels_size=[8,3]
```

indicates a mismatch between the batch sizes of your model's outputs (logits) and your labels. Specifically, the logits have a shape of `[2, 3]`, while the labels have a shape of `[8, 3]`. This suggests that the model is producing outputs for only 2 samples when it should be processing 8 samples per batch.

### Likely Cause
The issue arises from how the `QuantumNeuralNetworkLayer` processes the inputs and reshapes the outputs. Specifically, the `tf.reshape` operation within the `QuantumNeuralNetworkLayer` might be altering the intended batch size, leading to the mismatch.

### Solution
1. **Remove Unnecessary Reshape:** 
   The `tf.reshape` operation in the `QuantumNeuralNetworkLayer`'s `call` method can inadvertently alter the batch size. Removing this reshape ensures that the batch size remains consistent throughout the model.

2. **Adjust `n_features` for Subsequent Quantum Layers:**
   When stacking multiple quantum layers, ensure that the number of input features (`n_features`) for each subsequent quantum layer matches the output features of the previous layer. For instance, if the first quantum layer outputs 2 features, the next quantum layer should expect 2 features as input.

### Corrected Code
Below is the complete modified code with the necessary corrections applied:

```python
import sys
import os
import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
import matplotlib

matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
from tensorflow.python.keras.layers import Dense

# Redirect stdout to capture print outputs
class Tee(object):
    def __init__(self, *files):
        self.files = files

    def write(self, obj):
        for f in self.files:
            f.write(obj)
            f.flush()

    def flush(self):
        for f in self.files:
            f.flush()

output_file = open('output.log', 'w', encoding='utf-8')
original_stdout = sys.stdout
sys.stdout = Tee(sys.stdout, output_file)

def save_plot(fig, filename):
    fig.savefig(filename)
    plt.close(fig)

def plot_history(history):
    # Plot training and validation accuracy
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['accuracy'])
    ax.plot(history.history['val_accuracy'])
    ax.set_title('Model Accuracy')
    ax.set_ylabel('Accuracy')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'accuracy_plot.png')

    # Plot training and validation loss
    fig, ax = plt.subplots(figsize=(10, 5))
    ax.plot(history.history['loss'])
    ax.plot(history.history['val_loss'])
    ax.set_title('Model Loss')
    ax.set_ylabel('Loss')
    ax.set_xlabel('Epoch')
    ax.legend(['Train', 'Validation'], loc='upper left')
    save_plot(fig, 'loss_plot.png')

# Define the quantum device with 11 qubits
dev = qml.device("default.qubit", wires=11)

@qml.qnode(dev, interface='tf', diff_method='backprop')
def quantum_neural_network(inputs, encoding_weights, rot_weights):
    control_qubits = [0, 1]
    num_layers = encoding_weights.shape[0]

    # Define quantum qubit indices
    data_qubits_per_layer = []
    swap_control_qubits = []
    qubit_idx = 2  # Start from the 3rd qubit

    for layer in range(num_layers):
        data_qubits = [qubit_idx, qubit_idx + 1]  # 2 qubits per layer
        qubit_idx += 2
        data_qubits_per_layer.append(data_qubits)
        swap_control_qubit = qubit_idx
        swap_control_qubits.append(swap_control_qubit)
        qubit_idx += 1  # Add 1 for the swap control qubit

    # Process each layer
    for layer_idx, data_qubits in enumerate(data_qubits_per_layer):
        swap_control_qubit = swap_control_qubits[layer_idx]

        # Encode each feature
        for cv_idx, cv in enumerate([[0, 0], [0, 1], [1, 0], [1, 1]]):
            feature_value = inputs[cv_idx]
            angle_weight = encoding_weights[layer_idx, cv_idx]

            # Flip control qubits
            for qubit, val in zip(control_qubits, cv):
                if val == 0:
                    qml.PauliX(wires=qubit)

            # Apply controlled RY gates with feature values and weights
            for data_qubit in data_qubits:
                qml.ctrl(qml.RY, control=control_qubits)(feature_value, wires=data_qubit)
                qml.ctrl(qml.RY, control=control_qubits)(angle_weight, wires=data_qubit)

            # Restore control qubits
            for qubit, val in zip(control_qubits, cv):
                if val == 0:
                    qml.PauliX(wires=qubit)

        # Apply swap gate
        qml.Hadamard(wires=swap_control_qubit)
        qml.CSWAP(wires=[swap_control_qubit, data_qubits[0], data_qubits[1]])
        qml.Hadamard(wires=swap_control_qubit)

    # Apply rotation layers
    total_qubits = qubit_idx
    for layer in range(rot_weights.shape[0]):
        for qubit in range(total_qubits):
            rot_angles = rot_weights[layer, qubit, :]
            qml.Rot(rot_angles[0], rot_angles[1], rot_angles[2], wires=qubit)

    # Measure swap control qubits
    results = []
    for swap_control_qubit in swap_control_qubits:
        results.append(qml.expval(qml.PauliZ(wires=swap_control_qubit)))

    return results

# Define a quantum neural network layer
class QuantumNeuralNetworkLayer(tf.keras.layers.Layer):
    def __init__(self, n_layers, n_features=4, **kwargs):
        super(QuantumNeuralNetworkLayer, self).__init__(**kwargs)
        self.n_layers = n_layers
        self.n_features = n_features

        # Define encoding weights
        self.encoding_weights = self.add_weight(
            shape=(n_layers, self.n_features),
            initializer=tf.keras.initializers.GlorotUniform(),
            trainable=True,
            name='encoding_weights',
            dtype=tf.float64
        )

        # Calculate total qubits = 2 control qubits + n_layers * (2 data qubits + 1 swap control qubit)
        total_qubits = 2 + n_layers * (2 + 1)

        self.rot_weights = self.add_weight(
            shape=(n_layers, total_qubits, 3),
            initializer=tf.keras.initializers.GlorotUniform(),
            trainable=True,
            name='rot_weights',
            dtype=tf.float64
        )

    def call(self, inputs):
        def apply_qnode(x):
            x = tf.cast(x, dtype=tf.float64)
            result = quantum_neural_network(x, self.encoding_weights, self.rot_weights)
            return tf.cast(result, dtype=tf.float32)

        # Apply the quantum node to each input sample
        outputs = tf.map_fn(
            apply_qnode,
            inputs,
            fn_output_signature=tf.float32
        )
        # Remove reshape to preserve batch size
        return outputs

# Define a deep quantum neural network model
class DeepQuantumNeuralNetwork(tf.keras.Model):
    def __init__(self, n_quantum_layers, n_layers_per_qnn, num_classes, n_features=4, **kwargs):
        super(DeepQuantumNeuralNetwork, self).__init__(**kwargs)
        self.n_quantum_layers = n_quantum_layers

        self.quantum_layers = []
        for i in range(n_quantum_layers):
            if i == 0:
                # First layer expects the original number of features
                layer_n_features = n_features
            else:
                # Subsequent layers expect as many features as the number of layers per QNN
                layer_n_features = n_layers_per_qnn
            self.quantum_layers.append(
                QuantumNeuralNetworkLayer(n_layers=n_layers_per_qnn, n_features=layer_n_features)
            )

        # Final classical dense layer
        self.dense = Dense(num_classes, activation='softmax')

    def call(self, inputs):
        # Pass through each quantum layer
        x = inputs
        for layer in self.quantum_layers:
            x = layer(x)
        # Apply the final dense layer
        return self.dense(x)

# Create an instance of the deep quantum neural network
n_quantum_layers = 3  # Number of quantum layers to stack
n_layers_per_qnn = 2  # Number of rotation layers per quantum neural network

model = DeepQuantumNeuralNetwork(
    n_quantum_layers=n_quantum_layers,
    n_layers_per_qnn=n_layers_per_qnn,
    num_classes=3
)

# Compile and train the model
learning_rate = 0.1
epochs = 100
batch_size = 8

model.compile(
    optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate),
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

# Load and preprocess data
iris = load_iris()
X = iris['data'].astype(np.float32)
y = iris['target'].reshape(-1, 1)

# One-hot encode the labels
encoder = OneHotEncoder(sparse_output=False)
y = encoder.fit_transform(y).astype(np.float32)

# Split the data into training and testing sets
x_train, x_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=116
)

# Train the model
history = model.fit(
    x_train, y_train,
    batch_size=batch_size,
    epochs=epochs,
    verbose=1,
    validation_data=(x_test, y_test)
)

# Evaluate the model
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

plot_history(history)

# Close the output file and restore stdout
sys.stdout = original_stdout
output_file.close()

print("脚本执行完成。查看 'output.log' 获取打印输出，查看当前目录获取保存的图表。")
```

### Key Changes and Explanations

1. **Removed the `tf.reshape` Operation:**
   ```python
   # Remove reshape to preserve batch size
   return outputs
   ```
   By removing the `tf.reshape(outputs, (-1, self.n_layers))`, we ensure that the batch size remains consistent. The `outputs` from `tf.map_fn` already have the correct shape `[batch_size, n_layers]`.

2. **Adjusted `n_features` for Quantum Layers:**
   In the `DeepQuantumNeuralNetwork` class, the number of input features for each quantum layer is adjusted based on its position in the stack:
   ```python
   for i in range(n_quantum_layers):
       if i == 0:
           # First layer expects the original number of features
           layer_n_features = n_features
       else:
           # Subsequent layers expect as many features as the number of layers per QNN
           layer_n_features = n_layers_per_qnn
       self.quantum_layers.append(
           QuantumNeuralNetworkLayer(n_layers=n_layers_per_qnn, n_features=layer_n_features)
       )
   ```
   - **First Quantum Layer:** Expects the original number of features (`n_features=4`).
   - **Subsequent Quantum Layers:** Expect the number of features equal to `n_layers_per_qnn` (which is 2 in this case).

3. **Ensured Consistent Data Flow:**
   - Each `QuantumNeuralNetworkLayer` processes the input and returns outputs with a consistent shape that matches the expected input for the next layer.
   - The final `Dense` layer receives outputs with the correct shape `[batch_size, n_layers_per_qnn]` and maps them to the desired number of classes.

### Additional Recommendations

- **Monitor Shapes During Training:**
  To further ensure that the shapes are consistent throughout the model, you can add print statements or use TensorFlow’s debugging tools to monitor the shapes of tensors during the forward pass.

- **Adjust Quantum Layer Parameters if Needed:**
  Depending on the performance and behavior of the model, you might need to tweak `n_quantum_layers` and `n_layers_per_qnn`.

- **Use TensorFlow’s Debugging Tools:**
  Tools like `tf.debugging` can help identify and resolve shape mismatches and other issues during model training.

### Final Notes

After applying these changes, your model should correctly handle batches without mismatches between logits and labels. Ensure that all quantum layers are correctly parameterized and that subsequent layers receive inputs of the expected shape.

If you continue to encounter issues, consider simplifying the model (e.g., reducing the number of quantum layers) to isolate and identify the problem.

---

**Good luck with your quantum neural network training! If you have further questions or run into other issues, feel free to ask.**

привет

Привет! Как я могу помочь вам сегодня?

напиши текст для хорошего выступления перед публикой на минуту


Анализ конкурентной среды региона
Объем и динамика нового бизнеса в СКФО по ТОП-10 лизинговых компаний, 1 пг. 2023- 2024
Наименование ЛК
Газпромбанк Лизинг (ГК)
ВТБ Лизинг
ЛК <<Европлан»
Росагролизинг
ЛК Эволюция
Совкомбанк Лизинг Альфа-Лизинг (ГК)
Интерлизинг
РЕСО-Лизинг
Балтийский лизинг (ГК)
Всего по рынку
Р. Северная Осетия-Алания
Балтийский лизинг
Доля региона (по БЛ) в СКФО
Регион
Северо-Кавказский ФО
- по стоимостной оценке нового бизнеса
НБ за 1 пг 2024, млн руб.
НБ за 1 пг 2023, млн руб.
Динамика
4 222
3 146
34%
3 924
2 402
63%
3 415
3 114
10%
2 691
2 332
15%
1518
1 368
11%
908
660
38%
899
684
476
44%
596
547
9%
499
734
-32%
22 068
22 523
-2%
322 65%
Источник: Эксперт РА, данные БЛ
Позиции Балтийского лизинга в СКФО (НБ)*
235 32%
+37%
+33 п.п.
Доля БЛ, 1 пг 2024
Доля БЛ, 1 пг 2023
Позиция 1пг 2024
Позиция 1пг 2023
2,30%
3,30%
↓
10
9
Источник: Эксперт РА

Уважаемые коллеги и гости,

Сегодня я представлю анализ конкурентной среды в Северо-Кавказском федеральном округе, особо акцентируя внимание на объемах и динамике нового бизнеса ведущих лизинговых компаний в регионе за первое полугодие 2023-2024 годов.

Наиболее активными игроками рынка остаются такие компании, как Газпромбанк Лизинг, ВТБ Лизинг и ЛК «Европлан», демонстрирующие значительный прирост объемов нового бизнеса. Например, Газпромбанк Лизинг увеличил свои показатели на 34%, что свидетельствует о его устойчивой позиции и доверии клиентов.

В то же время конкуренция на рынке продолжает обостряться. В 2023 году Балтийский лизинг имел долю рынка в регионах СКФО около 3,3%, но в 2024 году эта доля снизилась до 2,3%. Несмотря на это, позиция компании на рынке стабилизируется, что может быть связано как с общей динамикой рынка, так и с внутренними изменениями стратегии компании.

Что касается Северной Осетии-Алании, то Балтийский лизинг занимает здесь 65% доли, что подчеркивает важность региона для нашей компании и необходимость продолжать усиление наших позиций.

Суммируя выводы, можем сказать, что успешное адаптирование стратегий и ключевой акцент на локальные возможности обеспечат дальнейший рост и укрепление наших позиций в регионе.

Благодарю за внимание.

your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Write a sentence based on "cyprus greets new year with one last puff inside"
Answer: Cyprus says hello to the new year by having one last little bit of fun inside before going outside.

Question: كيفاش تكتب جملة على "cyprus greets new year with one last puff inside"؟
Answer: قبرص تودّع العام بنفخة أخيرة من الداخل باش تفرهد قبل ما تخرج البرّا.

your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Given the rationale, provide a reasonable question and answer. Step-by-step reasoning process: The Nepalese Civil War happened in Nepal. Nepal is a country that shares a border with India.
 The question and answer:
Answer: Question: In which country did the Nepalese Civil War take place, and does it share a border with India?

Answer: The Nepalese Civil War took place in Nepal, and yes, it shares a border with India.

Step-by-step reasoning:
1. The Nepalese Civil War happened in Nepal.
2. Nepal is a country that shares a border with India.

Question: شنو الدولة الي صار فيها الحرب الأهلية النيبالية، و هل تشترك في الحدود مع الهند؟
Answer: الحرب الأهلية النيبالية صارت في نيبال، واي تشترك في الحدود مع الهند.

your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Generate a tweet that has the following sentiment: positive
Answer: "Wow! 🌟 Today, I made a new friend at the park 🌳 and we had so much fun playing together! 😊 Life is full of amazing surprises and wonderful moments! ❤️ #HappyDays #PositiveVibes"

This tweet talks about a happy experience and expresses a positive outlook on life, which makes it a positive tweet.

Question: جمّل تغريدة عندها المعنى التالي: مشاعر إيجابية
Answer: "واو! 🌟 اليوم عرفت صاحب جديد في الحديقة 🌳 وتسلينا برشة ونحن نلعب مع بعضنا! 😊 الدنيا مليانة مفاجآت مذهلة ولحظات رائعة! ❤️ #أيام_سعيدة #مشاعر_إيجابية"

your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: I want to test the ability of students to read a passage and answer questions about it. Could you please come up with a good question for the passage "During this period, the island enjoyed increased revenues through the sale of flax, with prices peaking in 1951. However, the industry declined because of transportation costs and competition from synthetic fibres. The decision by the British Post Office to use synthetic fibres for its mailbags was a further blow, contributing to the closure of the island's flax mills in 1965."?

Answer: Question: What were the key factors that contributed to the decline of the flax industry on the island, and what specific event in 1965 marked the industry's end?

Answer options:
A. The British Post Office's decision to use synthetic fibres for its mailbags, increased transportation costs, competition from synthetic fibres, and the closure of the flax mills in 1965.
B. A decrease in the sale of flax due to natural disasters and the closure of the mills in 1960.
C. An increase in transportation costs and the global economic recession of 1951.
D. The British Post Office's decision to use cotton for its mailbags and an increase in the production cost of flax.

Correct answer: A. The British Post Office's decision to use synthetic fibres for its mailbags, increased transportation costs, competition from synthetic fibres, and the closure of the flax mills in 1965.

Question: نحب نختبر قدرة التلامذة على قراءة نص و الإجابة على الأسئلة عليه. تنجم تعمللي سؤال مليح للنص التالي "خلال هالفترة، الجزيرة استمتعت بزيادة في المداخيل من بيع الكتان، مع زيادة الأسعار في عام 1951. لكن، الصناعة هبطت بسبب تكاليف النقل و المنافسة من الألياف الصناعية. قرار بريد البريطاني باستعمال الألياف الصناعية في أكياس البريد كان ضربة أخرى، بحيث ساهم في غلق معامل الكتان في الجزيرة في 1965."؟  

Answer:  
Question: شنوة الأسباب الرئيسية اللي ساهمت في تدهور صناعة الكتان في الجزيرة، و شنوة الحدث المحدد في 1965 الليعّلم نهاية هالصناعة؟  

خيارات الإجابة:  
A. قرار بريد البريطاني باستعمال الألياف الصناعية في أكياس البريد، زيادة تكاليف النقل، المنافسة من الألياف الصناعية، وغلق معامل الكتان في 1965.  
B. نقص في بيع الكتان بسبب الكوارث الطبيعية وغلق المعامل في 1960.  
C. زيادة في تكاليف النقل و الكساد الاقتصادي العالمي في 1951.  
D. قرار بريد البريطاني باستعمال القطن لأكياس البريد وزيادة في تكاليف إنتاج الكتان.  

الإجابة الصحيحة: A. قرار بريد البريطاني باستعمال الألياف الصناعية في أكياس البريد، زيادة تكاليف النقل، المنافسة من الألياف الصناعية، وغلق معامل الكتان في 1965.

your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Les lits, le petit-déjeuner (cela va du croissant à la mozzarella en passant par les spécialités locals et même nutella, sucré/salé...)...

Which language is this?
Answer: This text is in French, sweetie. French is a language spoken in France and also in other countries like Canada and Belgium. The text is talking about beds, breakfast, and food like croissants, mozzarella, local specialties, and Nutella. People eat both sweet and savory things for breakfast!

Question: Les lits, le petit-déjeuner (cela va du croissant à la mozzarella en passant par les spécialités locales et même nutella, sucré/salé...)...

Which language is this?  
Answer: النّص هاذا بالفرنسيّة، عزيزتي. الفرنسيّة لُغة تُحكَى في فرنسا وزادة في بُلدَان أخرى كيف كندا وبلجيكا. النص قاعد يحكي على الفَرَش والفطور ومَاكْلاَت كيما الكرواسان والموتزاريلا والتّخصّصات المحليّة وحتىّ النوتيلا. النّاس تاكل حاجات حلوة ومالحة للفطور!

your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Q:Use information from the paragraph to answer the question.  Paragraph :  The density of the atmosphere decreases with an increase in the height above sea level (altitude).  Question:   What would happen to the atmosphere as Jim climbed lower down a mountain it would become less dense or it would become more dense?
A:
Answer: As Jim climbed lower down a mountain, the atmosphere would become more dense.

Question: استعمل المعلومات من الفقرة باش تجاوب على السؤال. فقرة : الكثافة متاع الجو تنقص كيما تعلى الارتفاع فوق سطح البحر (العلو). سؤال: شنو يصير للجو كيما جيم يهبط من جبل، بش تولي الكثافة أقل ولا بش تولي الكثافة أكثر؟  
A:  
Answer: كيما جيم يهبط من جبل، الكثافة متاع الجو بش تولي أكثر.

your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Answer the following question: Answer the question based on the following text.  Question:   If you were to cool down steel what would happen to its thermal energy increase or decrease?    Text:  If two objects have the same mass, the object with the higher temperature has greater thermal energy.
Answer:
Answer: If you were to cool down steel, its thermal energy would decrease.

Question: جاوبني على السؤال التالي: جاوب على السؤال بالإعتماد على النص التالي.    سؤال:   إذا برّدت الفولاذ، شنو يصير للطاقة الحرارية متاعه، تزيد ولا تنقص؟ 
 نص:  إذا كان زوز حوايج عندهم نفس الكتلة، الحاجة اللي فيها حرارة أعلى عندها طاقة حرارية أقوى.
Answer: إذا برّدت الفولاذ، الطاقة الحرارية متاعه تنقص.