One or more processors receive hyperspectral band input, biometric input, and cognitive input as response input, from a user sampling a plurality of base foods, each base food prepared with a subset of ingredients and preparation techniques. The response input is transformed to a numeric representation of the respective input. Deep learning techniques are used to train an algorithm using the response data. A probabilistic ranking of base food is generated using unsupervised learning. Probability values of base food, ingredients, and preparation technique, associations preferred by the user, are generated, along with rules which define constraints associated with conditions for base food, ingredient, and preparation techniques, of user preferences. An objective function is generated that includes decision variables respectively aligned with constraints, and in response to optimizing the objective function, a preferred base food and ingredients, with preferred conditions of the user, is determined.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving, by one or more processors, somatic and cognitive response data of a user sampling a plurality of base foods, wherein each instance of a sample of a base food is prepared with a set of ingredients and preparation techniques from a plurality of ingredients and preparation techniques, and wherein the somatic and the cognitive response data includes labeled and unlabeled data; determining, by one or more processors, hierarchical features of the somatic and the cognitive response data utilizing unsupervised deep learning techniques of machine learning performed on the unlabeled data of the somatic and the cognitive response data that is received; generating by one or more processors, a probabilistic ranking of the user's preference of the plurality of base foods, based on results of the unsupervised deep learning techniques of machine learning; performing an association, by one or more processors, of one or more sets of ingredients and preparation techniques from the plurality of ingredients and preparation techniques, with one or more base foods of the plurality of base foods, respectively, based on supervised deep learning techniques of machine learning, using the somatic and the cognitive response data of responses that are labeled; generating, by one or more processors, a probability of the user preferring a base food of the plurality of base foods associated with a set of ingredients and preparation techniques of the plurality of ingredients and preparation techniques; generating by one or more processors, an objective function that includes a decision variable corresponding respectively to an element of the set of ingredients and preparation techniques; and in response to optimizing the objective function, one or more processors determining an amount of the element of the set of ingredients and preparation techniques to include in preparation of the base food preferred by the user. 2. The method of claim 1, wherein the unsupervised deep learning techniques of machine learning include providing the somatic and the cognitive input of unlabeled data to visible nodes of a stacked restricted Boltzman machine (RBM). 3. The method of claim 1, wherein the somatic and the cognitive response data of a user sampling a plurality of base foods includes hyperspectral band data, biometric data, behavioral response data, and audio response data, and user provided input to preference rating of sampled food. 4. The method of claim 1, wherein the somatic and the cognitive response data of a user sampling a plurality of base foods are transformed to a pixel-based image, wherein each pixel of the pixel-based image includes a numeric value representing a portion of the response input. 5. The method of claim 1, wherein the determination of the hierarchical features of the somatic and the cognitive response data, and the generation of a probabilistic ranking of the user preferring a base food of the plurality of base foods prepared are performed utilizing a neuromorphic chip configured for unsupervised deep learning techniques. 6. The method of claim 5, wherein the neuromorphic chip is trained to perform unsupervised deep learning techniques of machine learning by input of unlabeled response data to visible nodes of a stacked restricted Boltzman machine operating on the neuromorphic chip. 7. The method of claim 1, wherein the association of the one or more sets of ingredients and preparation techniques with one or more base foods is performed by utilizing a neuromorphic chip configured for supervised deep learning techniques. 8. The method of claim 1, further comprising: generating, by one or more processors, rules that define one or more constraints regarding the preferences of the user for base foods and one or more sets of ingredients and preparation techniques, wherein the rules are generated based on labeled input that is received regarding one or more conditions of the user. 9. The method of claim 1, wherein the optimization of the objective function is performed by linear programming. 10. The method of claim 1, wherein constraints are selected from a group consisting of: a cost of food and preparation, a time to prepare and cook the food, the availability of a base food and each ingredient, and a health condition of the user. 11. A computer program product, comprising: one or more computer-readable storage media having program instruction executable by one or more computer processors, the program instructions comprising: program instructions to receive somatic and cognitive response data of a user sampling a plurality of base foods, wherein each instance of a sample of a base food is prepared with a set of ingredients and preparation techniques from a plurality of ingredients and preparation techniques, and wherein the somatic and the cognitive response data includes labeled and unlabeled data; program instructions to determine hierarchical features of the somatic and the cognitive response data utilizing unsupervised deep learning techniques of machine learning performed on the unlabeled data of the somatic and the cognitive response data that is received; program instructions to generate a probabilistic ranking of the user's preference of the plurality of base foods, based on results of the unsupervised deep learning techniques of machine learning; program instructions to perform an association of one or more sets of ingredients and preparation techniques from the plurality of ingredients and preparation techniques, with one or more base foods of the plurality of base foods, respectively, based on supervised deep learning techniques of machine learning, using the somatic and the cognitive response data of responses that are labeled; program instructions to generate a probability of the user preferring a base food of the plurality of base foods associated with a set of ingredients and preparation techniques of the plurality of ingredients and preparation techniques; program instructions to generate an objective function that includes a decision variable corresponding respectively to an element of the set of ingredients and preparation techniques; and in response to optimizing the objective function, program instructions to determine an amount of the element of the set of ingredients and preparation techniques to include in preparation of the base food preferred by the user. 12. The computer program product of claim 11, wherein the unsupervised deep learning techniques of machine learning include providing the somatic and the cognitive input of unlabeled data to visible nodes of a stacked restricted Boltzman machine (RBM). 13. The computer program product of claim 11, wherein the somatic and the cognitive response data of a user sampling a plurality of base foods includes hyperspectral band data, biometric data, behavioral response data, and audio response data, and user provided input to preference rating of sampled food. 14. The computer program product of claim 11, wherein the somatic and the cognitive response data of a user sampling a plurality of base foods are transformed to a pixel-based image, wherein each pixel of the pixel-based image includes a numeric value representing a portion of the response input. 15. The computer program product of claim 11, wherein the determination of the hierarchical features of the somatic and the cognitive response data, and the generation of a probabilistic ranking of the user preferring a base food of the plurality of base foods prepared are performed utilizing a neuromorphic chip configured for unsupervised deep learning techniques. 16. A system comprising: one or more computer processors, one or more computer-readable storage media, program instructions stored on the one or more computer-readable storage media for execution by at least one of the one or more processors, the program instructions comprising: program instructions to receive somatic and cognitive response data of a user sampling a plurality of base foods, wherein each instance of a sample of a base food is prepared with a set of ingredients and preparation techniques from a plurality of ingredients and preparation techniques, and wherein the somatic and the cognitive response data includes labeled and unlabeled data; program instructions to determine hierarchical features of the somatic and the cognitive response data utilizing unsupervised deep learning techniques of machine learning performed on the unlabeled data of the somatic and the cognitive response data that is received; program instructions to generate a probabilistic ranking of the user's preference of the plurality of base foods, based on results of the unsupervised deep learning techniques of machine learning; program instructions to perform an association of one or more sets of ingredients and preparation techniques from the plurality of ingredients and preparation techniques, with one or more base foods of the plurality of base foods, respectively, based on supervised deep learning techniques of machine learning, using the somatic and the cognitive response data of responses that are labeled; program instructions to generate a probability of the user preferring a base food of the plurality of base foods associated with a set of ingredients and preparation techniques of the plurality of ingredients and preparation techniques; program instructions to generate an objective function that includes a decision variable corresponding respectively to an element of the set of ingredients and preparation techniques; and in response to optimizing the objective function, program instructions to determine an amount of the element of the set of ingredients and preparation techniques to include in preparation of the base food preferred by the user. 17. The system of claim 16, wherein the association of the one or more sets of ingredients and preparation techniques with one or more base foods is performed by utilizing a neuromorphic chip configured for supervised deep learning techniques. 18. The system of claim 16, wherein a particular decision variable, in response to having a constraint directed to an element of a set of ingredients and preparation techniques, includes consideration of the constraint in determination of a value for the particular decision variable performance of an optimization of the objective function. 19. The system of claim 16, wherein the optimization of the objective function is performed by linear programming. 20. The system of claim 16, wherein constraints are selected from a group consisting of: a cost of food and preparation, a time to prepare and cook the food, the availability of a base food and each ingredient, and a health condition of the user