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quantum-perceptron

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ashutosh1919 commited on Apr 15, 2023

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4da8a9d

1 Parent(s): 2b9363f

Added code to generate data and train model

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.DS_Store +0 -0
TrainPerceptron.ipynb +348 -0
data/sample_space_qubits_4_fweight_626.txt +0 -0
data/train_space_qubits_4_fweight_626.txt +3050 -0
quantum_perceptron/train/__init__.py +2 -0
quantum_perceptron/train/data_gen.py +90 -0
quantum_perceptron/train/training.py +164 -0
quantum_perceptron/utils/__init__.py +1 -0
quantum_perceptron/utils/data_utils.py +42 -1
quantum_perceptron/utils/plot_utils.py +41 -0
requirements.txt +2 -1

.DS_Store ADDED Viewed

Binary file (6.15 kB). View file

TrainPerceptron.ipynb ADDED Viewed

	@@ -0,0 +1,348 @@

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9f3fb515-747d-4e93-bf07-de5553a6e958",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from quantum_perceptron.utils import plot_img_from_data, get_vector_from_int, get_int_from_vector\n",
+    "from quantum_perceptron.train import generate_dataset, PerceptronTrainer\n",
+    "from quantum_perceptron import Perceptron"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "681740ed-ea82-428e-a5c8-9d44cfe92b2e",
+   "metadata": {},
+   "source": [
+    "# Generate Dataset\n",
+    "\n",
+    "Generate dataset to train 4 qubit perceptron."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "eba68da3-013e-4501-bdaa-67c82cef393d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fixed_weight = 626\n",
+    "num_qubits = 4\n",
+    "plot_img_from_data(fixed_weight, num_qubits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "18e6cc2b-51be-4d83-8e3a-4bbd4078bcf7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 65536/65536 [19:38<00:00, 55.59it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of positive samples: 274\n",
+      "Number of negative samples: 65262\n",
+      "Saved data to ./data/sample_space_qubits_4_fweight_626.txt\n",
+      "Saved training data to ./data/train_space_qubits_4_fweight_626.txt\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "generate_dataset(num_qubits=num_qubits, fixed_weight=fixed_weight)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ecd46307-eb53-46b4-809a-4493b1661d62",
+   "metadata": {},
+   "source": [
+    "# Perceptron Training\n",
+    "\n",
+    "Train generated dataset with 4 qubit perceptron. We will initialize random weight and will train perceptron so that it gradually updates the weight to the `fixed_weight` with which the dataset is generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b6081742-8913-4d46-a6bc-d62bc2eb715b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mashutosh1919\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "Tracking run with wandb version 0.14.2"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Run data is saved locally in <code>/Users/ashutosh1919/Documents/IUB/QuantumProgramming/quantum-perceptron/wandb/run-20230415_181130-p665sffu</code>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "Syncing run <strong><a href='https://wandb.ai/ashutosh1919/quantum-perceptron/runs/p665sffu' target=\"_blank\">vivid-lake-5</a></strong> to <a href='https://wandb.ai/ashutosh1919/quantum-perceptron' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View project at <a href='https://wandb.ai/ashutosh1919/quantum-perceptron' target=\"_blank\">https://wandb.ai/ashutosh1919/quantum-perceptron</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       " View run at <a href='https://wandb.ai/ashutosh1919/quantum-perceptron/runs/p665sffu' target=\"_blank\">https://wandb.ai/ashutosh1919/quantum-perceptron/runs/p665sffu</a>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer = PerceptronTrainer(\n",
+    "    num_qubits=num_qubits,\n",
+    "    fixed_weight=fixed_weight,\n",
+    "    dataset_path=\"./data/train_space_qubits_4_fweight_626.txt\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c07e3adf-43e8-4f70-9f53-4b7367d7df8f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Randomly initialized weight before training:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Randomly initialized weight before training:\")\n",
+    "plot_img_from_data(trainer.weight_variable, num_qubits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "92be5fcf-e82a-45a6-8e26-3b5bb89101c7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:56<00:00, 26.25it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:57<00:00, 26.03it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:55<00:00, 26.41it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:52<00:00, 27.15it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:49<00:00, 27.81it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:56<00:00, 26.14it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:58<00:00, 25.78it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:48<00:00, 27.99it/s]\n",
+      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3050/3050 [01:54<00:00, 26.72it/s]\n",
+      " 32%|████████████████████████████████████▋                                                                               | 963/3050 [00:35<01:17, 26.77it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training converged at step: 28414\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.train(num_epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "84c72b42-e18c-456f-b5a4-f41b577cb50b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final weight after training:\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(\"Final weight after training:\")\n",
+    "plot_img_from_data(trainer.weight_variable, num_qubits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fbecdf4f-68c8-4d4d-9a6f-9ef66bd182f0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Fidelity Similarity with Actual Weight')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(list(range(trainer.num_steps)), sorted(trainer.accumulate_loss))\n",
+    "plt.xlabel(\"Number of Steps\")\n",
+    "plt.ylabel(\"Fidelity Similarity with Actual Weight\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "quantum",
+   "language": "python",
+   "name": "quantum"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

data/sample_space_qubits_4_fweight_626.txt ADDED Viewed

The diff for this file is too large to render. See raw diff

data/train_space_qubits_4_fweight_626.txt ADDED Viewed

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quantum_perceptron/train/__init__.py ADDED Viewed

	@@ -0,0 +1,2 @@


1	+ from quantum_perceptron.train.data_gen import *
2	+ from quantum_perceptron.train.training import *

quantum_perceptron/train/data_gen.py ADDED Viewed

	@@ -0,0 +1,90 @@

+import numpy as np
+from tqdm import tqdm
+from quantum_perceptron.utils import (
+    calculate_succ_probability
+)
+from quantum_perceptron.perceptron import Perceptron
+def generate_training_samples(data: np.ndarray,
+                              num_positive_samples: int,
+                              num_negative_samples: int) -> np.ndarray:
+    """
+    From the entire dataset, generate training samples.
+    """
+    pos_inds = np.where(data[:, 1] == 1)[0]
+    neg_inds = np.where(data[:, 1] == 0)[0]
+    if len(pos_inds) < num_positive_samples:
+        num_positive_samples = len(pos_inds)
+    if len(neg_inds) < num_negative_samples:
+        num_negative_samples = len(neg_inds)
+    sampled_neg_inds = np.random.choice(neg_inds,
+                                        num_negative_samples,
+                                        replace=False)
+    sampled_pos_inds = np.random.choice(pos_inds,
+                                        num_positive_samples,
+                                        replace=False)
+    new_data = np.vstack((data[sampled_pos_inds], data[sampled_neg_inds]))
+    np.random.shuffle(new_data)
+    return new_data
+def generate_dataset(num_qubits: int = 4,
+                     fixed_weight: int = 626,
+                     dir_path: str = './data/',
+                     threshold: float = 0.5,
+                     num_runs: int = 8192,
+                     create_training_samples: bool = True,
+                     num_pos_train_samples: int = 50,
+                     num_neg_train_samples: int = 3000):
+    """
+    Generate training dataset with fixed weight.
+    Args:
+        num_qubits: `int` representing number of qubits.
+        fixed_weight: `int` representing the fixed weight value.
+        dir_path: `str` representing the directory path.
+    """
+    num_samples = np.power(2, np.power(2, num_qubits))
+    data = np.empty([num_samples, 2], dtype=np.int64)
+    p = Perceptron(num_qubits, fixed_weight, 0)
+    for i in tqdm(range(num_samples)):
+        p.input = i
+        p.build_circuit()
+        prob = calculate_succ_probability(p.measure_circuit(num_runs))
+        if prob > threshold:
+            label = 1
+        else:
+            label = 0
+        data[i][0] = i
+        data[i][1] = label
+    print("Number of positive samples: {}".format(
+        np.sum(data[:, 1] == 1)
+    ))
+    print("Number of negative samples: {}".format(
+        np.sum(data[:, 1] == 0)
+    ))
+    filename = 'sample_space_qubits_{}_fweight_{}.txt'.format(
+        num_qubits, fixed_weight
+    )
+    np.savetxt(dir_path + filename, data, fmt='%i,%i', delimiter=',')
+    print('Saved data to {}'.format(dir_path + filename))
+    if create_training_samples:
+        train_data = generate_training_samples(
+            data, num_pos_train_samples, num_neg_train_samples
+        )
+        train_filename = 'train_space_qubits_{}_fweight_{}.txt'.format(
+            num_qubits, fixed_weight
+        )
+        np.savetxt(dir_path + train_filename,
+                   train_data,
+                   fmt='%i,%i',
+                   delimiter=',')
+        print('Saved training data to {}'.format(dir_path + train_filename))

quantum_perceptron/train/training.py ADDED Viewed

	@@ -0,0 +1,164 @@

+import os
+from typing import List
+import numpy as np
+from tqdm import tqdm
+import wandb
+from quantum_perceptron.utils import (
+    get_vector_from_int,
+    get_int_from_vector,
+    calculate_succ_probability
+)
+from quantum_perceptron import Perceptron
+class PerceptronTrainer:
+    def __init__(self,
+                 num_qubits: int,
+                 fixed_weight: int,
+                 dataset_path: str,
+                 threshold: float = 0.5,
+                 num_runs: int = 8192,
+                 learning_rate_pos: float = 0.5,
+                 learning_rate_neg: float = 0.5):
+        """
+        This class is used to train the perceptron.
+        Args:
+          num_qubits: `int` representing number of qubits.
+          fixed_weight: `int` representing the fixed weight value.
+          dataset_path: `str` representing the path to the dataset.
+          threshold: `float` representing the threshold value.
+          num_runs: `int` representing number of runs.
+          learning_rate_pos: `float` representing the learning rate for positve
+            samples.
+          learning_rate_neg: `float` representing the learning rate for
+            negativesamples.
+        """
+        self.num_qubits = num_qubits
+        self.fixed_weight = fixed_weight
+        assert os.path.exists(dataset_path), "Dataset path does not exist"
+        self.data = self.read_dataset(dataset_path)
+        self.threshold = threshold
+        self.num_runs = num_runs
+        self.learning_rate_pos = learning_rate_pos
+        self.learning_rate_neg = learning_rate_neg
+        self.perceptron = Perceptron(num_qubits)
+        self.accumulate_loss: List[float] = []
+        self.num_steps = 0
+        # Initializing random weight for the training
+        self.weight_variable = np.random.randint(
+            np.power(2, np.power(2, num_qubits)))
+        wandb.init(
+            project="quantum-perceptron",
+            config={
+                "learning_rate_pos": learning_rate_pos,
+                "learning_rate_neg": learning_rate_neg,
+                "fixed_weight": fixed_weight,
+                "num_qubits": num_qubits,
+                "dataset": dataset_path,
+                "num_runs": num_runs,
+                "threshold": threshold
+            }
+        )
+    def read_dataset(self, filepath: str) -> np.ndarray:
+        """
+        Read dataset from file.
+        """
+        return np.loadtxt(filepath, dtype=np.int64, delimiter=',')
+    def invert_non_matching_bits(self, input: int):
+        """
+        Invert non-matching positions in vector.
+        """
+        input_vector = get_vector_from_int(input, self.num_qubits)
+        weight_vector = get_vector_from_int(self.weight_variable,
+                                            self.num_qubits)
+        non_match_ids = np.where(input_vector != weight_vector)[0]
+        num_select = int(np.ceil(len(non_match_ids) * self.learning_rate_pos))
+        selected_ids = np.random.choice(non_match_ids,
+                                        num_select,
+                                        replace=False)
+        for id in selected_ids:
+            weight_vector[id] *= -1
+        self.weight_variable = get_int_from_vector(weight_vector,
+                                                   self.num_qubits)
+    def invert_matching_bits(self, input: int):
+        """
+        Invert matching positions in vector.
+        """
+        input_vector = get_vector_from_int(input, self.num_qubits)
+        weight_vector = get_vector_from_int(self.weight_variable,
+                                            self.num_qubits)
+        match_ids = np.where(input_vector == weight_vector)[0]
+        num_select = int(np.ceil(len(match_ids) * self.learning_rate_neg))
+        selected_ids = np.random.choice(match_ids,
+                                        num_select,
+                                        replace=False)
+        for id in selected_ids:
+            weight_vector[id] *= -1
+        self.weight_variable = get_int_from_vector(weight_vector,
+                                                   self.num_qubits)
+    def calc_loss(self):
+        """
+        Note that we will only use this loss to generate the plot
+        and not for training the perceptron.
+        """
+        self.perceptron.input = self.weight_variable
+        self.perceptron.weight = self.fixed_weight
+        self.perceptron.build_circuit()
+        loss = calculate_succ_probability(
+            self.perceptron.measure_circuit(self.num_runs))
+        return loss
+    def train_step(self, input: int, label: int):
+        """
+        Training step for a single sample.
+        """
+        self.perceptron.input = input
+        self.perceptron.weight = self.weight_variable
+        self.perceptron.build_circuit()
+        prob = calculate_succ_probability(
+            self.perceptron.measure_circuit(self.num_runs))
+        loss = self.calc_loss()
+        self.accumulate_loss.append(loss)
+        self.num_steps += 1
+        if int(loss) == 1:
+            print("Training converged at step: {}".format(self.num_steps))
+            return True
+        if prob > self.threshold:
+            pred = 1
+        else:
+            pred = 0
+        if label == 1 and pred == 0:
+            self.invert_non_matching_bits(input)
+            wandb.log({"probability": loss, "weight": self.weight_variable})
+        elif label == 0 and pred == 1:
+            self.invert_matching_bits(input)
+            wandb.log({"probability": loss, "weight": self.weight_variable})
+        return False
+    def train_epoch(self, epoch: int):
+        """
+        Train the epoch.
+        """
+        for i in tqdm(range(self.data.shape[0])):
+            input = self.data[i, 0]
+            label = self.data[i, 1]
+            converged = self.train_step(input, label)
+            if converged:
+                return True
+        return False
+    def train(self, num_epochs: int):
+        """
+        Train the perceptron.
+        """
+        for i in range(num_epochs):
+            converged = self.train_epoch(i)
+            if converged:
+                break

quantum_perceptron/utils/__init__.py CHANGED Viewed

@@ -1,2 +1,3 @@
 from quantum_perceptron.utils.data_utils import *
 from quantum_perceptron.utils.quantum_utils import *

 from quantum_perceptron.utils.data_utils import *
 from quantum_perceptron.utils.quantum_utils import *
+from quantum_perceptron.utils.plot_utils import *

quantum_perceptron/utils/data_utils.py CHANGED Viewed

@@ -35,7 +35,7 @@ def get_vector_from_int(data: int, num_qubits: int) -> np.ndarray:
     Args:
       data: `int` representing data value
-        (correspponding toinput or weight vector)
       num_qubits: `int` representing number of qubits.
     Returns: Vector in  form of `np.ndarray`.
@@ -52,6 +52,30 @@ def get_vector_from_int(data: int, num_qubits: int) -> np.ndarray:
     return data_vector
 def get_possible_state_strings(num_bits: int) -> np.ndarray:
     """
     Get all the state bit strings corresponding to given number of bits.
@@ -100,3 +124,20 @@ def get_ones_counts_to_states(states: np.ndarray) -> Dict[int, List[int]]:
         ones_count[ct].append(i)
     return ones_count

     Args:
       data: `int` representing data value
+        (correspponding to input or weight vector)
       num_qubits: `int` representing number of qubits.
     Returns: Vector in  form of `np.ndarray`.
     return data_vector
+def get_int_from_vector(data_vector: np.ndarray, num_qubits: int) -> int:
+    """
+    Get the integer data value from the vector.
+    Args:
+      data_vector: `np.ndarray` representing the vector.
+      num_qubits: `int` representing number of qubits.
+    Returns: `int` representing the data value.
+    """
+    if len(data_vector) != np.power(2, num_qubits):
+        raise ValueError("The vector length is not equal to 2^num_qubits")
+    data_bin_vec = []
+    for i, val in enumerate(data_vector):
+        if val == -1:
+            data_bin_vec.append('1')
+        elif val == 1:
+            data_bin_vec.append('0')
+    data_bin = ''.join(data_bin_vec)
+    return int(data_bin, 2)
 def get_possible_state_strings(num_bits: int) -> np.ndarray:
     """
     Get all the state bit strings corresponding to given number of bits.
         ones_count[ct].append(i)
     return ones_count
+def calculate_succ_probability(counts: Dict[str, int]) -> float:
+    """
+    Calculate the success probability from the counts of the states.
+    Args:
+      counts: `dict` containing the counts of the states.
+    Returns: `float` representing the success probability.
+    """
+    if len(counts) == 0:
+        raise ValueError("The counts dict is empty")
+    total_count = sum(counts.values())
+    succ_count = counts.get('1', 0)
+    return succ_count / total_count

quantum_perceptron/utils/plot_utils.py ADDED Viewed

	@@ -0,0 +1,41 @@

+import numpy as np
+import matplotlib.pyplot as plt
+from quantum_perceptron.utils.data_utils import (
+    get_bin_int,
+    assert_bits,
+    assert_negative
+)
+def get_img_from_data(data: int, num_qubits: int) -> np.ndarray:
+    """
+    Get n x n matrix representing the image of the data where n is
+    num_qubits.
+    Args:
+      data: `int` representing data value
+        (correspponding to input or weight vector)
+      num_qubits: `int` representing number of qubits.
+    Returns: Image in  form of `np.ndarray`.
+    """
+    assert_negative(data)
+    assert_bits(data, num_qubits)
+    bin_str = get_bin_int(data, num_qubits)
+    img = np.zeros((np.power(2, num_qubits)))
+    for i, bit in enumerate(bin_str):
+        if bit == '0':
+            img[i] = 255
+    return img.reshape((num_qubits, num_qubits))
+def plot_img_from_data(data: int, num_qubits: int):
+    """
+    Plot image from data.
+    """
+    img = get_img_from_data(data, num_qubits)
+    ax = plt.imshow(img, cmap='gray')
+    ax.axes.xaxis.set_visible(False)
+    ax.axes.yaxis.set_visible(False)

requirements.txt CHANGED Viewed

@@ -3,4 +3,5 @@ qiskit
 pycodestyle
 pytest
 mypy
-gradio

 pycodestyle
 pytest
 mypy
+gradio
+wandb